01467nas a2200145   4500008004100000020002200041245004900063210004800112260003800160520097900198100001901177700002301196700001901219856008301238       2023                        eng d  a978-3-031-44067-000aExplaining Black-Boxes in Federated Learning0 aExplaining BlackBoxes in Federated Learning  aChambSpringer Nature Switzerland3 aFederated Learning has witnessed increasing popularity in the past few years for its ability to train Machine Learning models in critical contexts, using private data without moving them. Most of the work in the literature proposes algorithms and architectures for training neural networks, which although they present high performance in different predicting tasks and are easy to be learned with a cooperative mechanism, their predictive reasoning is obscure. Therefore, in this paper, we propose a variant of SHAP, one of the most widely used explanation methods, tailored to Horizontal server-based Federated Learning. The basic idea is having the possibility to explain an instance's prediction performed by the trained Machine Leaning model as an aggregation of the explanations provided by the clients participating in the cooperation. We empirically test our proposal on two different tabular datasets, and we observe interesting and encouraging preliminary results.1 aCorbucci, Luca1 aGuidotti, Riccardo1 aMonreale, Anna  uhttps://kdd.isti.cnr.it/publications/explaining-black-boxes-federated-learning01472nas a2200157   4500008004100000245006500041210006500106520092000171100002201091700002601113700001901139700002301158700002101181700002001202856009201222       2022                        eng d00aExplaining Black Box with visual exploration of Latent Space0 aExplaining Black Box with visual exploration of Latent Space3 aAutoencoders are a powerful yet opaque feature reduction technique, on top of which we propose a novel way for the joint visual exploration of both latent and real space. By interactively exploiting the mapping between latent and real features, it is possible to unveil the meaning of latent features while providing deeper insight into the original variables. To achieve this goal, we exploit and re-adapt existing approaches from eXplainable Artificial Intelligence (XAI) to understand the relationships between the input and latent features. The uncovered relationships between input features and latent ones allow the user to understand the data structure concerning external variables such as the predictions of a classification model. We developed an interactive framework that visually explores the latent space and allows the user to understand the relationships of the input features with model prediction.1 aBodria, Francesco1 aRinzivillo, Salvatore1 aFadda, Daniele1 aGuidotti, Riccardo1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://diglib.eg.org/xmlui/bitstream/handle/10.2312/evs20221098/085-089.pdf?sequence=101553nas a2200133   4500008004100000245006800041210006700109260001300176520111900189100001901308700002301327700002001350856004901370       2022                        eng d00aExplaining Siamese Networks in Few-Shot Learning for Audio Data0 aExplaining Siamese Networks in FewShot Learning for Audio Data  bSpringer3 aMachine learning models are not able to generalize correctly when queried on samples belonging to class distributions that were never seen during training. This is a critical issue, since real world applications might need to quickly adapt without the necessity of re-training. To overcome these limitations, few-shot learning frameworks have been proposed and their applicability has been studied widely for computer vision tasks. Siamese Networks learn pairs similarity in form of a metric that can be easily extended on new unseen classes. Unfortunately, the downside of such systems is the lack of explainability. We propose a method to explain the outcomes of Siamese Networks in the context of few-shot learning for audio data. This objective is pursued through a local perturbation-based approach that evaluates segments-weighted-average contributions to the final outcome considering the interplay between different areas of the audio spectrogram. Qualitative and quantitative results demonstrate that our method is able to show common intra-class characteristics and erroneous reliance on silent sections.1 aFedele, Andrea1 aGuidotti, Riccardo1 aPedreschi, Dino  uhttps://doi.org/10.1007/978-3-031-18840-4_3600645nas a2200157   4500008004100000245010000041210006900141100002300210700001900233700002400252700002300276700001900299700002000318700002100338856012800359       2022                        eng d00aStable and actionable explanations of black-box models through factual and counterfactual rules0 aStable and actionable explanations of blackbox models through fa1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aNaretto, Francesca1 aTurini, Franco1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/stable-and-actionable-explanations-black-box-models-through-factual-and-counterfactual00523nas a2200157   4500008004100000245007200041210006900113490001900182100002200201700002100223700002300244700002300267700002000290700001800310856003700328       2021                        eng d00aBenchmarking and Survey of Explanation Methods for Black Box Models0 aBenchmarking and Survey of Explanation Methods for Black Box Mod0 vabs/2102.130761 aBodria, Francesco1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aNaretto, Francesca1 aPedreschi, Dino1 aRinzivillo, S  uhttps://arxiv.org/abs/2102.1307603888nas a2200589   4500008004100000020001400041245010600055210006900161260001500230520215400245100001702399700002302416700002802439700002102467700002202488700001802510700002702528700002302555700001702578700001602595700001802611700002102629700002602650700002102676700002102697700002302718700001802741700001702759700002102776700001802797700001702815700001902832700001702851700002902868700001902897700002102916700001802937700002302955700002202978700002003000700001903020700001903039700002303058700001803081700002403099700001703123700001803140700002203158700002603180700002703206856006503233       2021                        eng d  a1572-843900aGive more data, awareness and control to individual citizens, and they will help COVID-19 containment0 aGive more data awareness and control to individual citizens and   c2021/02/023 aThe rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.1 aNanni, Mirco1 aAndrienko, Gennady1 aBarabasi, Albert-Laszlo1 aBoldrini, Chiara1 aBonchi, Francesco1 aCattuto, Ciro1 aChiaromonte, Francesca1 aComandé, Giovanni1 aConti, Marco1 aCoté, Mark1 aDignum, Frank1 aDignum, Virginia1 aDomingo-Ferrer, Josep1 aFerragina, Paolo1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aHelbing, Dirk1 aKaski, Kimmo1 aKertész, János1 aLehmann, Sune1 aLepri, Bruno1 aLukowicz, Paul1 aMatwin, Stan1 aJiménez, David, Megías1 aMonreale, Anna1 aMorik, Katharina1 aOliver, Nuria1 aPassarella, Andrea1 aPasserini, Andrea1 aPedreschi, Dino1 aPentland, Alex1 aPianesi, Fabio1 aPratesi, Francesca1 aRinzivillo, S1 aRuggieri, Salvatore1 aSiebes, Arno1 aTorra, Vicenc1 aTrasarti, Roberto1 avan den Hoven, Jeroen1 aVespignani, Alessandro  uhttps://link.springer.com/article/10.1007/s10676-020-09572-w02525nas a2200205   4500008004100000020001400041245007100055210006900126260001600195300001100211490000800222520189700230100001802127700002302145700001902168700001902187700002002206700002102226856007202247       2021                        eng d  a0004-370200aGLocalX - From Local to Global Explanations of Black Box AI Models0 aGLocalX From Local to Global Explanations of Black Box AI Models  c2021/05/01/  a1034570 v2943 aArtificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLocalX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLocalX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLocalX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLocalX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.1 aSetzu, Mattia1 aGuidotti, Riccardo1 aMonreale, Anna1 aTurini, Franco1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://www.sciencedirect.com/science/article/pii/S000437022100008402976nas a2200373   4500008004100000020002200041245008700063210006900150260005200219520183300271100002102104700001902125700001802144700002002162700002102182700002102203700001902224700002402243700001802267700002302285700001902308700001902327700002102346700001802367700001802385700001902403700002002422700001602442700002002458700001902478700002002497700001802517856006702535       2020                        eng d  a978-3-030-54994-700aAnalysis and Visualization of Performance Indicators in University Admission Tests0 aAnalysis and Visualization of Performance Indicators in Universi  aChambSpringer International Publishingc2020//3 aThis paper presents an analytical platform for evaluation of the performance and anomaly detection of tests for admission to public universities in Italy. Each test is personalized for each student and is composed of a series of questions, classified on different domains (e.g. maths, science, logic, etc.). Since each test is unique for composition, it is crucial to guarantee a similar level of difficulty for all the tests in a session. For this reason, to each question, it is assigned a level of difficulty from a domain expert. Thus, the general difficultness of a test depends on the correct classification of each item. We propose two approaches to detect outliers. A visualization-based approach using dynamic filter and responsive visual widgets. A data mining approach to evaluate the performance of the different questions for five years. We used clustering to group the questions according to a set of performance indicators to provide labeling of the data-driven level of difficulty. The measured level is compared with the a priori assigned by experts. The misclassifications are then highlighted to the expert, who will be able to refine the question or the classification. Sequential pattern mining is used to check if biases are present in the composition of the tests and their performance. This analysis is meant to exclude overlaps or direct dependencies among questions. Analyzing co-occurrences we are able to state that the composition of each test is fair and uniform for all the students, even on several sessions. The analytical results are presented to the expert through a visual web application that loads the analytical data and indicators and composes an interactive dashboard. The user may explore the patterns and models extracted by filtering and changing thresholds and analytical parameters.1 aNatilli, Michela1 aFadda, Daniele1 aRinzivillo, S1 aPedreschi, Dino1 aLicari, Federica1 aSekerinski, Emil1 aMoreira, Nelma1 aOliveira, José, N.1 aRatiu, Daniel1 aGuidotti, Riccardo1 aFarrell, Marie1 aLuckcuck, Matt1 aMarmsoler, Diego1 aCampos, José1 aAstarte, Troy1 aGonnord, Laure1 aCerone, Antonio1 aCouto, Luis1 aDongol, Brijesh1 aKutrib, Martin1 aMonteiro, Pedro1 aDelmas, David  uhttps://link.springer.com/chapter/10.1007/978-3-030-54994-7_1401953nas a2200229   4500008004100000020002200041245008200063210006900145260005200214520119500266100002301461700001901484700001701503700002001520700001701540700001901557700001901576700001701595700002201612700002201634856006701656       2020                        eng d  a978-3-030-46150-800aBlack Box Explanation by Learning Image Exemplars in the Latent Feature Space0 aBlack Box Explanation by Learning Image Exemplars in the Latent   aChambSpringer International Publishingc2020//3 aWe present an approach to explain the decisions of black box models for image classification. While using the black box to label images, our explanation method exploits the latent feature space learned through an adversarial autoencoder. The proposed method first generates exemplar images in the latent feature space and learns a decision tree classifier. Then, it selects and decodes exemplars respecting local decision rules. Finally, it visualizes them in a manner that shows to the user how the exemplars can be modified to either stay within their class, or to become counter-factuals by “morphing” into another class. Since we focus on black box decision systems for image classification, the explanation obtained from the exemplars also provides a saliency map highlighting the areas of the image that contribute to its classification, and areas of the image that push it into another class. We present the results of an experimental evaluation on three datasets and two black box models. Besides providing the most useful and interpretable explanations, we show that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability.1 aGuidotti, Riccardo1 aMonreale, Anna1 aMatwin, Stan1 aPedreschi, Dino1 aBrefeld, Ulf1 aFromont, Elisa1 aHotho, Andreas1 aKnobbe, Arno1 aMaathuis, Marloes1 aRobardet, Céline  uhttps://link.springer.com/chapter/10.1007/978-3-030-46150-8_1201756nas a2200193   4500008004100000020002200041245008700063210006900150260005200219520106600271100002301337700002301360700002401383700002101407700002501428700002501453700001701478856006701495       2020                        eng d  a978-3-030-61527-700aExplaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars0 aExplaining Sentiment Classification with Synthetic Exemplars and  aChambSpringer International Publishingc2020//3 aWe present xspells, a model-agnostic local approach for explaining the decisions of a black box model for sentiment classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences – albeit they are synthetically generated. xspells generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. We report experiments on two datasets showing that xspells outperforms the well-known lime method in terms of quality of explanations, fidelity, and usefulness, and that is comparable to it in terms of stability.1 aLampridis, Orestis1 aGuidotti, Riccardo1 aRuggieri, Salvatore1 aAppice, Annalisa1 aTsoumakas, Grigorios1 aManolopoulos, Yannis1 aMatwin, Stan  uhttps://link.springer.com/chapter/10.1007/978-3-030-61527-7_2401510nas a2200181   4500008004100000020002200041245004300063210004300106260005200149520094000201100001801141700002301159700001901182700001901201700001901220700002001239856006901259       2020                        eng d  a978-3-030-43823-400aGlobal Explanations with Local Scoring0 aGlobal Explanations with Local Scoring  aChambSpringer International Publishingc2020//3 aArtificial Intelligence systems often adopt machine learning models encoding complex algorithms with potentially unknown behavior. As the application of these “black box” models grows, it is our responsibility to understand their inner working and formulate them in human-understandable explanations. To this end, we propose a rule-based model-agnostic explanation method that follows a local-to-global schema: it generalizes a global explanation summarizing the decision logic of a black box starting from the local explanations of single predicted instances. We define a scoring system based on a rule relevance score to extract global explanations from a set of local explanations in the form of decision rules. Experiments on several datasets and black boxes show the stability, and low complexity of the global explanations provided by the proposed solution in comparison with baselines and state-of-the-art global explainers.1 aSetzu, Mattia1 aGuidotti, Riccardo1 aMonreale, Anna1 aTurini, Franco1 aCellier, Peggy1 aDriessens, Kurt  uhttps://link.springer.com/chapter/10.1007%2F978-3-030-43823-4_1401787nas a2200313   4500008004100000020001400041245004600055210004500101260001500146300001100161520090000172100001701072700002301089700002301112700002101135700001701156700002101173700002301194700002001217700001401237700002901251700002101280700002301301700002001324700002001344700002301364700001901387856006701406       2020                        eng d  a2364-416800aHuman migration: the big data perspective0 aHuman migration the big data perspective  c2020/03/23  a1–203 aHow can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.1 aSirbu, Alina1 aAndrienko, Gennady1 aAndrienko, Natalia1 aBoldrini, Chiara1 aConti, Marco1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aBertoli, Simone1 aKim, Jisu1 aMuntean, Cristina, Ioana1 aPappalardo, Luca1 aPassarella, Andrea1 aPedreschi, Dino1 aPollacci, Laura1 aPratesi, Francesca1 aSharma, Rajesh  uhttps://link.springer.com/article/10.1007%2Fs41060-020-00213-502009nas a2200337   4500008004100000020002200041245008900063210006900152260005200221520095500273100002301228700002101251700002101272700001901293700002401312700001801336700002301354700001901377700001901396700002101415700001801436700001801454700001901472700002001491700001601511700002001527700001901547700002001566700001801586856006701604       2020                        eng d  a978-3-030-54994-700a“Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network0 aKnow Thyself How Personal Music Tastes Shape the LastFm Online S  aChambSpringer International Publishingc2020//3 aAs Nietzsche once wrote “Without music, life would be a mistake” (Twilight of the Idols, 1889.). The music we listen to reflects our personality, our way to approach life. In order to enforce self-awareness, we devised a Personal Listening Data Model that allows for capturing individual music preferences and patterns of music consumption. We applied our model to 30k users of Last.Fm for which we collected both friendship ties and multiple listening. Starting from such rich data we performed an analysis whose final aim was twofold: (i) capture, and characterize, the individual dimension of music consumption in order to identify clusters of like-minded Last.Fm users; (ii) analyze if, and how, such clusters relate to the social structure expressed by the users in the service. Do there exist individuals having similar Personal Listening Data Models? If so, are they directly connected in the social graph or belong to the same community?.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aSekerinski, Emil1 aMoreira, Nelma1 aOliveira, José, N.1 aRatiu, Daniel1 aGuidotti, Riccardo1 aFarrell, Marie1 aLuckcuck, Matt1 aMarmsoler, Diego1 aCampos, José1 aAstarte, Troy1 aGonnord, Laure1 aCerone, Antonio1 aCouto, Luis1 aDongol, Brijesh1 aKutrib, Martin1 aMonteiro, Pedro1 aDelmas, David  uhttps://link.springer.com/chapter/10.1007/978-3-030-54994-7_1102783nas a2200517   4500008004100000020002200041245008500063210006900148260005200217520120400269100002301473700002501496700002701521700002101548700002101569700001801590700002101608700002201629700001901651700002501670700002301695700002201718700002201740700002101762700001601783700002001799700002601819700002401845700002001869700002201889700002301911700002001934700001901954700002201973700002001995700002002015700002002035700001802055700001902073700002302092700001802115700002002133700002402153700002102177856006702198       2020                        eng d  a978-3-030-65965-300aPrediction and Explanation of Privacy Risk on Mobility Data with Neural Networks0 aPrediction and Explanation of Privacy Risk on Mobility Data with  aChambSpringer International Publishingc2020//3 aThe analysis of privacy risk for mobility data is a fundamental part of any privacy-aware process based on such data. Mobility data are highly sensitive. Therefore, the correct identification of the privacy risk before releasing the data to the public is of utmost importance. However, existing privacy risk assessment frameworks have high computational complexity. To tackle these issues, some recent work proposed a solution based on classification approaches to predict privacy risk using mobility features extracted from the data. In this paper, we propose an improvement of this approach by applying long short-term memory (LSTM) neural networks to predict the privacy risk directly from original mobility data. We empirically evaluate privacy risk on real data by applying our LSTM-based approach. Results show that our proposed method based on a LSTM network is effective in predicting the privacy risk with results in terms of F1 of up to 0.91. Moreover, to explain the predictions of our model, we employ a state-of-the-art explanation algorithm, Shap. We explore the resulting explanation, showing how it is possible to provide effective predictions while explaining them to the end-user.1 aNaretto, Francesca1 aPellungrini, Roberto1 aNardini, Franco, Maria1 aGiannotti, Fosca1 aKoprinska, Irena1 aKamp, Michael1 aAppice, Annalisa1 aLoglisci, Corrado1 aAntonie, Luiza1 aZimmermann, Albrecht1 aGuidotti, Riccardo1 aÖzgöbek, Özlem1 aRibeiro, Rita, P.1 aGavaldà, Ricard1 aGama, João1 aAdilova, Linara1 aKrishnamurthy, Yamuna1 aFerreira, Pedro, M.1 aMalerba, Donato1 aMedeiros, Ibéria1 aCeci, Michelangelo1 aManco, Giuseppe1 aMasciari, Elio1 aRas, Zbigniew, W.1 aChristen, Peter1 aNtoutsi, Eirini1 aSchubert, Erich1 aZimek, Arthur1 aMonreale, Anna1 aBiecek, Przemyslaw1 aRinzivillo, S1 aKille, Benjamin1 aLommatzsch, Andreas1 aGulla, Jon, Atle  uhttps://link.springer.com/chapter/10.1007/978-3-030-65965-3_3402108nas a2200217   4500008004100000245005300041210005100094520145500145100002301600700002301623700002101646700002201667700001801689700002001707700002101727700002101748700002201769700002301791700001101814856006501825       2020                        eng d00a(So) Big Data and the transformation of the city0 aSo Big Data and the transformation of the city3 aThe exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.1 aAndrienko, Gennady1 aAndrienko, Natalia1 aBoldrini, Chiara1 aCaldarelli, Guido1 aCintia, Paolo1 aCresci, Stefano1 aFacchini, Angelo1 aGiannotti, Fosca1 aGionis, Aristides1 aGuidotti, Riccardo1 aothers  uhttps://link.springer.com/article/10.1007/s41060-020-00207-300389nas a2200121   4500008004100000245004100041210003700082300001200119100002300131700001900154700002000173856007400193       2019                        eng d00aThe AI black box Explanation Problem0 aAI black box Explanation Problem  a12–131 aGuidotti, Riccardo1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/ai-black-box-explanation-problem00539nas a2200109   4500008004100000245012600041210006900167100002200236700002100258700002300279856012700302       2019                        eng d00aDefining Geographic Markets from Probabilistic Clusters: A Machine Learning Algorithm Applied to Supermarket Scanner Data0 aDefining Geographic Markets from Probabilistic Clusters A Machin1 aBruestle, Stephen1 aPappalardo, Luca1 aGuidotti, Riccardo  uhttps://kdd.isti.cnr.it/publications/defining-geographic-markets-probabilistic-clusters-machine-learning-algorithm-applied01664nas a2200145   4500008004100000245007300041210006900114260001300183520117100196100002301367700002301390700001901413700002001432856006601452       2019                        eng d00aExplaining multi-label black-box classifiers for health applications0 aExplaining multilabel blackbox classifiers for health applicatio  bSpringer3 aToday the state-of-the-art performance in classification is achieved by the so-called “black boxes”, i.e. decision-making systems whose internal logic is obscure. Such models could revolutionize the health-care system, however their deployment in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of transparency. The typical classification problem in health-care requires a multi-label approach since the possible labels are not mutually exclusive, e.g. diagnoses. We propose MARLENA, a model-agnostic method which explains multi-label black box decisions. MARLENA explains an individual decision in three steps. First, it generates a synthetic neighborhood around the instance to be explained using a strategy suitable for multi-label decisions. It then learns a decision tree on such neighborhood and finally derives from it a decision rule that explains the black box decision. Our experiments show that MARLENA performs well in terms of mimicking the black box behavior while gaining at the same time a notable amount of interpretability through compact decision rules, i.e. rules with limited length.1 aPanigutti, Cecilia1 aGuidotti, Riccardo1 aMonreale, Anna1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-030-24409-5_901554nas a2200157   4500008004100000245007400041210006900115520102800184100002301212700001901235700002101254700002001275700002401295700001901319856005801338       2019                        eng d00aFactual and Counterfactual Explanations for Black Box Decision Making0 aFactual and Counterfactual Explanations for Black Box Decision M3 aThe rise of sophisticated machine learning models has brought accurate but obscure decision systems, which hide their logic, thus undermining transparency, trust, and the adoption of artificial intelligence (AI) in socially sensitive and safety-critical contexts. We introduce a local rule-based explanation method, providing faithful explanations of the decision made by a black box classifier on a specific instance. The proposed method first learns an interpretable, local classifier on a synthetic neighborhood of the instance under investigation, generated by a genetic algorithm. Then, it derives from the interpretable classifier an explanation consisting of a decision rule, explaining the factual reasons of the decision, and a set of counterfactuals, suggesting the changes in the instance features that would lead to a different outcome. Experimental results show that the proposed method outperforms existing approaches in terms of the quality of the explanations and of the accuracy in mimicking the black box.1 aGuidotti, Riccardo1 aMonreale, Anna1 aGiannotti, Fosca1 aPedreschi, Dino1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://ieeexplore.ieee.org/abstract/document/892013801257nas a2200133   4500008004100000245009600041210006900137260001300206520077300219100002300992700001901015700002301034856006601057       2019                        eng d00aInvestigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers0 aInvestigating Neighborhood Generation Methods for Explanations o  bSpringer3 aGiven the wide use of machine learning approaches based on opaque prediction models, understanding the reasons behind decisions of black box decision systems is nowadays a crucial topic. We address the problem of providing meaningful explanations in the widely-applied image classification tasks. In particular, we explore the impact of changing the neighborhood generation function for a local interpretable model-agnostic explanator by proposing four different variants. All the proposed methods are based on a grid-based segmentation of the images, but each of them proposes a different strategy for generating the neighborhood of the image for which an explanation is required. A deep experimentation shows both improvements and weakness of each proposed approach.1 aGuidotti, Riccardo1 aMonreale, Anna1 aCariaggi, Leonardo  uhttps://link.springer.com/chapter/10.1007/978-3-030-16148-4_500501nas a2200109   4500008004100000245009000041210006900131260001300200100002300213700002100236856013400257       2019                        eng d00a“Know Thyself” How Personal Music Tastes Shape the Last. Fm Online Social Network0 aKnow Thyself How Personal Music Tastes Shape the Last Fm Online   bSpringer1 aGuidotti, Riccardo1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/%E2%80%9Cknow-thyself%E2%80%9D-how-personal-music-tastes-shape-last-fm-online-social-network01943nas a2200157   4500008004100000245006100041210006100102520143800163100002001601700002101621700002301642700001901665700002401684700001901708856005801727       2019                        eng d00aMeaningful explanations of Black Box AI decision systems0 aMeaningful explanations of Black Box AI decision systems3 aBlack box AI systems for automated decision making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We focus on the urgent open challenge of how to construct meaningful explanations of opaque AI/ML systems, introducing the local-toglobal framework for black box explanation, articulated along three lines: (i) the language for expressing explanations in terms of logic rules, with statistical and causal interpretation; (ii) the inference of local explanations for revealing the decision rationale for a specific case, by auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of many local explanations into simple global ones, with algorithms that optimize for quality and comprehensibility. We argue that the local-first approach opens the door to a wide variety of alternative solutions along different dimensions: a variety of data sources (relational, text, images, etc.), a variety of learning problems (multi-label classification, regression, scoring, ranking), a variety of languages for expressing meaningful explanations, a variety of means to audit a black box.1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://aaai.org/ojs/index.php/AAAI/article/view/505001650nas a2200301   4500008004100000020002200041245004800063210004800111260005200159520072600211100002500937700001900962700002300981700001901004700001901023700001901042700002101061700002001082700002201102700002101124700002301145700002101168700002401189700002301213700002201236700002301258856006701281       2019                        eng d  a978-3-030-13463-100aPrivacy Risk for Individual Basket Patterns0 aPrivacy Risk for Individual Basket Patterns  aChambSpringer International Publishingc2019//3 aRetail data are of fundamental importance for businesses and enterprises that want to understand the purchasing behaviour of their customers. Such data is also useful to develop analytical services and for marketing purposes, often based on individual purchasing patterns. However, retail data and extracted models may also provide very sensitive information to possible malicious third parties. Therefore, in this paper we propose a methodology for empirically assessing privacy risk in the releasing of individual purchasing data. The experiments on real-world retail data show that although individual patterns describe a summary of the customer activity, they may be successful used for the customer re-identifiation.1 aPellungrini, Roberto1 aMonreale, Anna1 aGuidotti, Riccardo1 aAlzate, Carlos1 aMonreale, Anna1 aBioglio, Livio1 aBitetta, Valerio1 aBordino, Ilaria1 aCaldarelli, Guido1 aFerretti, Andrea1 aGuidotti, Riccardo1 aGullo, Francesco1 aPascolutti, Stefano1 aPensa, Ruggero, G.1 aRobardet, Céline1 aSquartini, Tiziano  uhttps://link.springer.com/chapter/10.1007/978-3-030-13463-1_1101172nas a2200121   4500008004100000245004500041210003800086260000900124520081200133100002300945700002400968856005800992       2019                        eng d00aOn The Stability of Interpretable Models0 aStability of Interpretable Models  bIEEE3 aInterpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process. Bias in data collection and preparation, or in model's construction may severely affect the accountability of the design process. We conduct an experimental study of the stability of interpretable models with respect to feature selection, instance selection, and model selection. Our conclusions should raise awareness and attention of the scientific community on the need of a stability impact assessment of interpretable models.1 aGuidotti, Riccardo1 aRuggieri, Salvatore  uhttps://ieeexplore.ieee.org/abstract/document/885215801264nas a2200109   4500008004100000245005200041210005200093520088000145100002301025700002401048856008201072       2018                        eng d00aAssessing the Stability of Interpretable Models0 aAssessing the Stability of Interpretable Models3 aInterpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process, which, in particular, comprises data collection and filtering. Selection bias in data collection or in data pre-processing may affect the model learned. Although model induction algorithms are designed to learn to generalize, they pursue optimization of predictive accuracy. It remains unclear how interpretability is instead impacted. We conduct an experimental analysis to investigate whether interpretable models are able to cope with data selection bias as far as interpretability is concerned.1 aGuidotti, Riccardo1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/assessing-stability-interpretable-models02705nas a2200181   4500008004100000245007900041210006900120260001200189300000600201490000600207520211900213100002302332700002302355700001902378700002002397700002102417856008502438       2018                        eng d00aDiscovering temporal regularities in retail customers’ shopping behavior0 aDiscovering temporal regularities in retail customers shopping b  c01/2018  a60 v73 aIn this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customer’s temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity.1 aGuidotti, Riccardo1 aGabrielli, Lorenzo1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0133-001125nas a2200145   4500008004100000245007000041210006900111260001900180520063500199100002300834700002000857700001700877700001900894856006600913       2018                        eng d00aExplaining successful docker images using pattern mining analysis0 aExplaining successful docker images using pattern mining analysi  bSpringer, Cham3 aDocker is on the rise in today’s enterprise IT. It permits shipping applications inside portable containers, which run from so-called Docker images. Docker images are distributed in public registries, which also monitor their popularity. The popularity of an image directly impacts on its usage, and hence on the potential revenues of its developers. In this paper, we present a frequent pattern mining-based approach for understanding how to improve an image to increase its popularity. The results in this work can provide valuable insights to Docker image providers, helping them to design more competitive software products.1 aGuidotti, Riccardo1 aSoldani, Jacopo1 aNeri, Davide1 aBrogi, Antonio  uhttps://link.springer.com/chapter/10.1007/978-3-030-04771-9_900558nas a2200133   4500008004100000245009000041210006900131260001300200100002100213700001900234700002300253700002100276856012700297       2018                        eng d00aExploring Students Eating Habits Through Individual Profiling and Clustering Analysis0 aExploring Students Eating Habits Through Individual Profiling an  bSpringer1 aNatilli, Michela1 aMonreale, Anna1 aGuidotti, Riccardo1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/exploring-students-eating-habits-through-individual-profiling-and-clustering-analysis01229nas a2200157   4500008004100000245008100041210006900122260001300191520069500204100002000899700002300919700002100942700002100963700002000984856006701004       2018                        eng d00aThe Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis0 aFractal Dimension of Music Geography Popularity and Sentiment An  bSpringer3 aNowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a “fractal” musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians’ popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_1901318nas a2200157   4500008004100000245006900041210006900110260001300179520080200192100002300994700002001017700001701037700001901054700002001073856006701093       2018                        eng d00aHelping your docker images to spread based on explainable models0 aHelping your docker images to spread based on explainable models  bSpringer3 aDocker is on the rise in today’s enterprise IT. It permits shipping applications inside portable containers, which run from so-called Docker images. Docker images are distributed in public registries, which also monitor their popularity. The popularity of an image impacts on its actual usage, and hence on the potential revenues for its developers. In this paper, we present a solution based on interpretable decision tree and regression trees for estimating the popularity of a given Docker image, and for understanding how to improve an image to increase its popularity. The results presented in this work can provide valuable insights to Docker developers, helping them in spreading their images. Code related to this paper is available at: https://github.com/di-unipi-socc/DockerImageMiner.1 aGuidotti, Riccardo1 aSoldani, Jacopo1 aNeri, Davide1 aBrogi, Antonio1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-030-10997-4_1301364nas a2200157   4500008004100000245003700041210003300078300001100111520091500122100002001037700002301057700002101080700002101101700002001122856006401142       2018                        eng d00aThe italian music superdiversity0 aitalian music superdiversity  a1–233 aGlobalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs’ melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/article/10.1007/s11042-018-6511-601443nas a2200133   4500008004100000245002500041210002500066260000900091520110000100100002301200700001901223700001801242856004901260       2018                        eng d00aLearning Data Mining0 aLearning Data Mining  c20183 aIn the last decade the usage and study of data mining and machine learning algorithms have received an increasing attention from several and heterogeneous fields of research. Learning how and why a certain algorithm returns a particular result, and understanding which are the main problems connected to its execution is a hot topic in the education of data mining methods. In order to support data mining beginners, students, teachers, and researchers we introduce a novel didactic environment. The Didactic Data Mining Environment (DDME) allows to execute a data mining algorithm on a dataset and to observe the algorithm behavior step by step to learn how and why a certain result is returned. DDME can be practically exploited by teachers and students for having a more interactive learning of data mining. Indeed, on top of the core didactic library, we designed a visual platform that allows online execution of experiments and the visualization of the algorithm steps. The visual platform abstracts the coding activity and makes available the execution of algorithms to non-technicians.
1 aGuidotti, Riccardo1 aMonreale, Anna1 aRinzivillo, S  uhttps://ieeexplore.ieee.org/document/863145300538nas a2200145   4500008004100000245006400041210006300105100002300168700001900191700002400210700002000234700001900254700002100273856009800294       2018                        eng d00aLocal Rule-Based Explanations of Black Box Decision Systems0 aLocal RuleBased Explanations of Black Box Decision Systems1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aPedreschi, Dino1 aTurini, Franco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/local-rule-based-explanations-black-box-decision-systems00599nas a2200157   4500008004100000245007700041210006900118100002000187700002100207700002300228700001900251700002100270700002400291700001900315856010700334       2018                        eng d00aOpen the Black Box Data-Driven Explanation of Black Box Decision Systems0 aOpen the Black Box DataDriven Explanation of Black Box Decision 1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aMonreale, Anna1 aPappalardo, Luca1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/open-black-box-data-driven-explanation-black-box-decision-systems01676nas a2200145   4500008004100000245008600041210006900127520117000196100002301366700002101389700002101410700002101431700002001452856005801472       2018                        eng d00aPersonalized Market Basket Prediction with Temporal Annotated Recurring Sequences0 aPersonalized Market Basket Prediction with Temporal Annotated Re3 aNowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aPappalardo, Luca1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://ieeexplore.ieee.org/abstract/document/847715701683nas a2200181   4500008004100000245005600041210005400097300000700151490000700158520116300165100002301328700001901351700002401370700001901394700002101413700002001434856004701454       2018                        eng d00aA survey of methods for explaining black box models0 asurvey of methods for explaining black box models  a930 v513 aIn recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aTurini, Franco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://dl.acm.org/doi/abs/10.1145/323600901663nas a2200157   4500008004100000245006500041210006500106260000800171520113100179100002301310700001901333700001701352700002101369700002001390856009501410       2017                        eng d00aClustering Individual Transactional Data for Masses of Users0 aClustering Individual Transactional Data for Masses of Users  bACM3 aMining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. Transactional data is a very pervasive kind of information that is collected by several services, often involving huge pools of users. We propose txmeans, a parameter-free clustering algorithm able to efficiently partitioning transactional data in a completely automatic way. Txmeans is designed for the case where clustering must be applied on a massive number of different datasets, for instance when a large set of users need to be analyzed individually and each of them has generated a long history of transactions. A deep experimentation on both real and synthetic datasets shows the practical effectiveness of txmeans for the mass clustering of different personal datasets, and suggests that txmeans outperforms existing methods in terms of quality and efficiency. Finally, we present a personal cart assistant application based on txmeans1 aGuidotti, Riccardo1 aMonreale, Anna1 aNanni, Mirco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/clustering-individual-transactional-data-masses-users00434nas a2200109   4500008004100000245006600041210005900107260001900166100002300185700002000208856009600228       2017                        eng d00aOn the Equivalence Between Community Discovery and Clustering0 aEquivalence Between Community Discovery and Clustering  bSpringer, Cham1 aGuidotti, Riccardo1 aCoscia, Michele  uhttps://kdd.isti.cnr.it/publications/equivalence-between-community-discovery-and-clustering01239nas a2200157   4500008004100000245008100041210006900122260001900191520069900210100002000909700002300929700002100952700002100973700002000994856006701014       2017                        eng d00aThe Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis0 aFractal Dimension of Music Geography Popularity and Sentiment An  bSpringer, Cham3 aNowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a “fractal” musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians’ popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.

1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_1901445nas a2200169   4500008004100000245003500041210003500076300000800111490001000119520096400129100001701093700001901110700002301129700002201152700002001174856008101194       2017                        eng d00aICON Loop Carpooling Show Case0 aICON Loop Carpooling Show Case  a3100 v101013 aIn this chapter we describe a proactive carpooling service that combines induction and optimization mechanisms to maximize the impact of carpooling within a community. The approach autonomously infers the mobility demand of the users through the analysis of their mobility traces (i.e. Data Mining of GPS trajectories) and builds the network of all possible ride sharing opportunities among the users. Then, the maximal set of carpooling matches that satisfy some standard requirements (maximal capacity of vehicles, etc.) is computed through Constraint Programming models, and the resulting matches are proactively proposed to the users. Finally, in order to maximize the expected impact of the service, the probability that each carpooling match is accepted by the users involved is inferred through Machine Learning mechanisms and put in the CP model. The whole process is reiterated at regular intervals, thus forming an instance of the general ICON loop.1 aNanni, Mirco1 aKotthoff, Lars1 aGuidotti, Riccardo1 aO'Sullivan, Barry1 aPedreschi, Dino  uhttps://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf#page=31401536nas a2200157   4500008004100000245008700041210006900128260000900197520094200206100002301148700002101171700002101192700002101213700002001234856012401254       2017                        eng d00aMarket Basket Prediction using User-Centric Temporal Annotated Recurring Sequences0 aMarket Basket Prediction using UserCentric Temporal Annotated Re  bIEEE3 aNowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer’s decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern named Temporal Annotated Recurring Sequence (TARS). We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer’s stocks and recommend the set of most necessary items. A deep experimentation shows that TARS can explain the customers’ purchase behavior, and that TBP outperforms the state-of-the-art competitors.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aPappalardo, Luca1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/market-basket-prediction-using-user-centric-temporal-annotated-recurring-sequences01609nas a2200169   4500008004100000245005400041210005300095260001200148300001400160490000700174520108700181100002201268700002301290700001901313700002101332856008601353       2017                        eng d00aMyWay: Location prediction via mobility profiling0 aMyWay Location prediction via mobility profiling  c03/2017  a350–3670 v643 aForecasting the future positions of mobile users is a valuable task allowing us to operate efficiently a myriad of different applications which need this type of information. We propose MyWay, a prediction system which exploits the individual systematic behaviors modeled by mobility profiles to predict human movements. MyWay provides three strategies: the individual strategy uses only the user individual mobility profile, the collective strategy takes advantage of all users individual systematic behaviors, and the hybrid strategy that is a combination of the previous two. A key point is that MyWay only requires the sharing of individual mobility profiles, a concise representation of the user׳s movements, instead of raw trajectory data revealing the detailed movement of the users. We evaluate the prediction performances of our proposal by a deep experimentation on large real-world data. The results highlight that the synergy between the individual and collective knowledge is the key for a better prediction and allow the system to outperform the state-of-art methods.1 aTrasarti, Roberto1 aGuidotti, Riccardo1 aMonreale, Anna1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/myway-location-prediction-mobility-profiling01912nas a2200169   4500008004100000245006500041210006400106300001400170490000700184520135600191100002301547700001701570700001801587700002001605700002101625856009601646       2017                        eng d00aNever drive alone: Boosting carpooling with network analysis0 aNever drive alone Boosting carpooling with network analysis  a237–2570 v643 aCarpooling, i.e., the act where two or more travelers share the same car for a common trip, is one of the possibilities brought forward to reduce traffic and its externalities, but experience shows that it is difficult to boost the adoption of carpooling to significant levels. In our study, we analyze the potential impact of carpooling as a collective phenomenon emerging from people׳s mobility, by network analytics. Based on big mobility data from travelers in a given territory, we construct the network of potential carpooling, where nodes correspond to the users and links to possible shared trips, and analyze the structural and topological properties of this network, such as network communities and node ranking, to the purpose of highlighting the subpopulations with higher chances to create a carpooling community, and the propensity of users to be either drivers or passengers in a shared car. Our study is anchored to reality thanks to a large mobility dataset, consisting of the complete one-month-long GPS trajectories of approx. 10% circulating cars in Tuscany. We also analyze the aggregated outcome of carpooling by means of empirical simulations, showing how an assignment policy exploiting the network analytic concepts of communities and node rankings minimizes the number of single occupancy vehicles observed after carpooling.1 aGuidotti, Riccardo1 aNanni, Mirco1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/never-drive-alone-boosting-carpooling-network-analysis01626nas a2200145   4500008004100000245006300041210006300104520117000167100002301337700002101360700002101381700002101402700002001423856003701443       2017                        eng d00aNext Basket Prediction using Recurring Sequential Patterns0 aNext Basket Prediction using Recurring Sequential Patterns3 aNowadays, a hot challenge for supermarket chains is to offer personalized services for their customers. Next basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable to capture at the same time the different factors influencing the customer's decision process: co-occurrency, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aPappalardo, Luca1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://arxiv.org/abs/1702.0715801370nas a2200121   4500008004100000245008000041210006900121260001300190520093200203100002301135700002301158856006701181       2017                        eng d00aRecognizing Residents and Tourists with Retail Data Using Shopping Profiles0 aRecognizing Residents and Tourists with Retail Data Using Shoppi  bSpringer3 aThe huge quantity of personal data stored by service providers registering customers daily life enables the analysis of individual fingerprints characterizing the customers’ behavioral profiles. We propose
a framework for recognizing residents, tourists and occasional shoppers
among the customers of a retail market chain. We employ our recognition framework on a real massive dataset containing the shopping transactions of more than one million of customers, and we identify representative temporal shopping profiles for residents, tourists and occasional
customers. Our experiments show that even though residents are about
33% of the customers they are responsible for more than 90% of the expenditure. We statistically validate the number of residents and tourists
with national official statistics enabling in this way the adoption of our
recognition framework for the development of novel services and analysis.1 aGuidotti, Riccardo1 aGabrielli, Lorenzo  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_3500584nas a2200145   4500008004100000245009100041210006900132260001600201100002300217700002200240700001700262700002100279700002000300856011800320       2017                        eng d00aThere's A Path For Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas0 aTheres A Path For Everyone A DataDriven Personal Model Reproduci  aTokyobIEEE1 aGuidotti, Riccardo1 aTrasarti, Roberto1 aNanni, Mirco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/theres-path-everyone-data-driven-personal-model-reproducing-mobility-agendas01172nas a2200133   4500008004100000245009500041210006900136260002800205520061000233100002000843700002300863700002100886856013100907       2016                        eng d00a“Are we playing like Music-Stars?” Placing Emerging Artists on the Italian Music Scene0 aAre we playing like MusicStars Placing Emerging Artists on the I  aRiva del Gardac09/20163 aThe Italian emerging bands chase success on the footprint of popular artists by playing rhythmic danceable and happy songs. Our finding comes out from a deep study of the Italian music scene and how the new generation ofmusicians relate with the tradition of their country. By analyzing Spotify data we investigated the peculiarity of regional mu- sic and we placed emerging bands within the musical movements defined by already successful artists. The approach proposed and the results ob- tained are a first attempt to outline some rules suggesting how to reach the success in the musical Italian scene.1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/%E2%80%9Care-we-playing-music-stars%E2%80%9D-placing-emerging-artists-italian-music-scene01416nas a2200133   4500008004100000245001900041210001900060260001300079520107000092100002301162700002101185700002001206856005601226       2016                        eng d00aAudio Ergo Sum0 aAudio Ergo Sum  bSpringer3 aNobody can state “Rock is my favorite genre” or “David Bowie is my favorite artist”. We defined a Personal Listening Data Model able to capture musical preferences through indicators and patterns, and we discovered that we are all characterized by a limited set of musical preferences, but not by a unique predilection. The empowered capacity of mobile devices and their growing adoption in our everyday life is generating an enormous increment in the production of personal data such as calls, positioning, online purchases and even music listening. Musical listening is a type of data that has started receiving more attention from the scientific community as consequence of the increasing availability of rich and punctual online data sources. Starting from the listening of 30k Last.Fm users, we show how the employment of the Personal Listening Data Models can provide higher levels of self-awareness. In addition, the proposed model will enable the development of a wide range of analysis and musical services both at personal and at collective level.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/audio-ergo-sum01694nas a2200157   4500008004100000245006800041210006700109260003800176300001200214520112100226100002301347700002001370700002001390700002401410856010201434       2016                        eng d00aGoing Beyond GDP to Nowcast Well-Being Using Retail Market Data0 aGoing Beyond GDP to Nowcast WellBeing Using Retail Market Data  bSpringer International Publishing  a29–423 aOne of the most used measures of the economic health of a nation is the Gross Domestic Product (GDP): the market value of all officially recognized final goods and services produced within a country in a given period of time. GDP, prosperity and well-being of the citizens of a country have been shown to be highly correlated. However, GDP is an imperfect measure in many respects. GDP usually takes a lot of time to be estimated and arguably the well-being of the people is not quantifiable simply by the market value of the products available to them. In this paper we use a quantification of the average sophistication of satisfied needs of a population as an alternative to GDP. We show that this quantification can be calculated more easily than GDP and it is a very promising predictor of the GDP value, anticipating its estimation by six months. The measure is arguably a more multifaceted evaluation of the well-being of the population, as it tells us more about how people are satisfying their needs. Our study is based on a large dataset of retail micro transactions happening across the Italian territory.1 aGuidotti, Riccardo1 aCoscia, Michele1 aPedreschi, Dino1 aPennacchioli, Diego  uhttps://kdd.isti.cnr.it/publications/going-beyond-gdp-nowcast-well-being-using-retail-market-data01970nas a2200193   4500008004100000022001400041245010300055210006900158260001200227300000700239490000600246520137200252100002101624700002301645700001901668700002001687700002101707856004801728       2016                        eng d  a1869-546900aA supervised approach for intra-/inter-community interaction prediction in dynamic social networks0 asupervised approach for intraintercommunity interaction predicti  c09/2016  a860 v63 aDue to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario, one of the most challenging tasks involves the prediction of future interactions between couples of actors (i.e., users in online social networks, researchers in collaboration networks). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits features computed by time-aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intra-community and inter-community link prediction. Experimental results on real time-stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.1 aRossetti, Giulio1 aGuidotti, Riccardo1 aMiliou, Ioanna1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1007/s13278-016-0397-y01716nas a2200169   4500008004100000245006700041210006700108300000700175490000600182520115300188100002301341700001901364700001801383700002001401700002101421856010401442       2016                        eng d00aUnveiling mobility complexity through complex network analysis0 aUnveiling mobility complexity through complex network analysis  a590 v63 aThe availability of massive digital traces of individuals is offering a series of novel insights on the understanding of patterns characterizing human mobility. Many studies try to semantically enrich mobility data with annotations about human activities. However, these approaches either focus on places with high frequencies (e.g., home and work), or relay on background knowledge (e.g., public available points of interest). In this paper, we depart from the concept of frequency and we focus on a high level representation of mobility using network analytics. The visits of each driver to each systematic destination are modeled as links in a bipartite network where a set of nodes represents drivers and the other set represents places. We extract such network from two real datasets of human mobility based, respectively, on GPS and GSM data. We introduce the concept of mobility complexity of drivers and places as a ranking analysis over the nodes of these networks. In addition, by means of community discovery analysis, we differentiate subgroups of drivers and places according both to their homogeneity and to their mobility complexity.1 aGuidotti, Riccardo1 aMonreale, Anna1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/unveiling-mobility-complexity-through-complex-network-analysis01665nas a2200133   4500008004100000245008800041210006900129260003800198300001200236520111700248100002301365700002501388856011801413       2016                        eng d00aWhere Is My Next Friend? Recommending Enjoyable Profiles in Location Based Services0 aWhere Is My Next Friend Recommending Enjoyable Profiles in Locat  bSpringer International Publishing  a65–783 aHow many of your friends, with whom you enjoy spending some time, live close by? How many people are at your reach, with whom you could have a nice conversation? We introduce a measure of enjoyability that may be the basis for a new class of location-based services aimed at maximizing the likelihood that two persons, or a group of people, would enjoy spending time together. Our enjoyability takes into account both topic similarity between two users and the users’ tendency to connect to people with similar or dissimilar interest. We computed the enjoyability on two datasets of geo-located tweets, and we reasoned on the applicability of the obtained results for producing friend recommendations. We aim at suggesting couples of users which are not friends yet, but which are frequently co-located and maximize our enjoyability measure. By taking into account the spatial dimension, we show how 50 % of users may find at least one enjoyable person within 10 km of their two most visited locations. Our results are encouraging, and open the way for a new class of recommender systems based on enjoyability.1 aGuidotti, Riccardo1 aBerlingerio, Michele  uhttps://kdd.isti.cnr.it/publications/where-my-next-friend-recommending-enjoyable-profiles-location-based-services01914nas a2200145   4500008004100000245005100041210005100092260001600143520143700159100002301596700002001619700002001639700002401659856008501683       2015                        eng d00aBehavioral Entropy and Profitability in Retail0 aBehavioral Entropy and Profitability in Retail  aParisbIEEE3 aHuman behavior is predictable in principle: people are systematic in their everyday choices. This predictability can be used to plan events and infrastructure, both for the public good and for private gains. In this paper we investigate the largely unexplored relationship between the systematic behavior of a customer and its profitability for a retail company. We estimate a customer’s behavioral entropy over two dimensions: the basket entropy is the variety of what customers buy, and the spatio-temporal entropy is the spatial and temporal variety of their shopping sessions. To estimate the basket and the spatiotemporal entropy we use data mining and information theoretic techniques. We find that predictable systematic customers are more profitable for a supermarket: their average per capita expenditures are higher than non systematic customers and they visit the shops more often. However, this higher individual profitability is masked by its overall level. The highly systematic customers are a minority of the customer set. As a consequence, the total amount of revenues they generate is small. We suggest that favoring a systematic behavior in their customers might be a good strategy for supermarkets to increase revenue. These results are based on data coming from a large Italian supermarket chain, including more than 50 thousand customers visiting 23 shops to purchase more than 80 thousand distinct products.1 aGuidotti, Riccardo1 aCoscia, Michele1 aPedreschi, Dino1 aPennacchioli, Diego  uhttps://kdd.isti.cnr.it/publications/behavioral-entropy-and-profitability-retail01284nas a2200145   4500008004100000245006500041210006400106260004400170520074700214100001900961700001700980700002300997700002201020856009601042       2015                        eng d00aFind Your Way Back: Mobility Profile Mining with Constraints0 aFind Your Way Back Mobility Profile Mining with Constraints  aCorkbSpringer International Publishing3 aMobility profile mining is a data mining task that can be formulated as clustering over movement trajectory data. The main challenge is to separate the signal from the noise, i.e. one-off trips. We show that standard data mining approaches suffer the important drawback that they cannot take the symmetry of non-noise trajectories into account. That is, if a trajectory has a symmetric equivalent that covers the same trip in the reverse direction, it should become more likely that neither of them is labelled as noise. We present a constraint model that takes this knowledge into account to produce better clusters. We show the efficacy of our approach on real-world data that was previously processed using standard data mining techniques.1 aKotthoff, Lars1 aNanni, Mirco1 aGuidotti, Riccardo1 aO'Sullivan, Barry  uhttps://kdd.isti.cnr.it/publications/find-your-way-back-mobility-profile-mining-constraints01436nas a2200169   4500008004100000020002200041245007800063210006900141260002400210520086700234100002101101700002301122700002401145700002001169700002101189856005601210       2015                        eng d  a978-1-4503-3854-700aInteraction Prediction in Dynamic Networks exploiting Community Discovery0 aInteraction Prediction in Dynamic Networks exploiting Community   aParis, FrancebIEEE3 aDue to the growing availability of online social services, interactions between people became more and more easy to establish and track. Online social human activities generate digital footprints, that describe complex, rapidly evolving, dynamic networks. In such scenario one of the most challenging task to address involves the prediction of future interactions between couples of actors. In this study, we want to leverage networks dynamics and community structure to predict which are the future interactions more likely to appear. To this extent, we propose a supervised learning approach which exploit features computed by time-aware forecasts of topological measures calculated between pair of nodes belonging to the same community. Our experiments on real dynamic networks show that the designed analytical process is able to achieve interesting results.1 aRossetti, Giulio1 aGuidotti, Riccardo1 aPennacchioli, Diego1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dl.acm.org/citation.cfm?doid=2808797.280940100796nas a2200157   4500008004100000245005100041210005000092260000900142520030900151100001500460700002100475700001600496700002300512700002300535856008000558       2015                        eng d00aManaging travels with PETRA: The Rome use case0 aManaging travels with PETRA The Rome use case  bIEEE3 aThe aim of the PETRA project is to provide the basis for a city-wide transportation system that supports policies catering for both individual preferences of users and city-wide travel patterns. The PETRA platform will be initially deployed in the partner city of Rome, and later in Venice, and Tel-Aviv.1 aBotea, Adi1 aBraghin, Stefano1 aLopes, Nuno1 aGuidotti, Riccardo1 aPratesi, Francesca  uhttps://kdd.isti.cnr.it/publications/managing-travels-petra-rome-use-case-000973nas a2200181   4500008004100000245004900041210004900090260003800139520039600177100002500573700001600598700001500614700002100629700001600650700002300666700002300689856007900712       2015                        eng d00aMobility Mining for Journey Planning in Rome0 aMobility Mining for Journey Planning in Rome  bSpringer International Publishing3 aWe present recent results on integrating private car GPS routines obtained by a Data Mining module. into the PETRA (PErsonal TRansport Advisor) platform. The routines are used as additional “bus lines”, available to provide a ride to travelers. We present the effects of querying the planner with and without the routines, which show how Data Mining may help Smarter Cities applications.1 aBerlingerio, Michele1 aBicer, Veli1 aBotea, Adi1 aBraghin, Stefano1 aLopes, Nuno1 aGuidotti, Riccardo1 aPratesi, Francesca  uhttps://kdd.isti.cnr.it/publications/mobility-mining-journey-planning-rome00553nas a2200145   4500008004100000245007400041210006900115260000900184100002300193700001800216700002500234700002400259700002000283856010400303       2015                        eng d00aSocial or green? A data-driven approach for more enjoyable carpooling0 aSocial or green A datadriven approach for more enjoyable carpool  bIEEE1 aGuidotti, Riccardo1 aSassi, Andrea1 aBerlingerio, Michele1 aPascale, Alessandra1 aGhaddar, Bissan  uhttps://kdd.isti.cnr.it/publications/social-or-green-data-driven-approach-more-enjoyable-carpooling00406nas a2200109   4500008004100000245007700041210006900118100002300187700002200210700001700232856004700249       2015                        eng d00a{TOSCA:} two-steps clustering algorithm for personal locations detection0 aTOSCA twosteps clustering algorithm for personal locations detec1 aGuidotti, Riccardo1 aTrasarti, Roberto1 aNanni, Mirco  uhttp://doi.acm.org/10.1145/2820783.282081800491nas a2200121   4500008004100000245006900041210006900110260003100179300001400210100002300224700001800247856010400265       2015                        eng d00aTowards a Boosted Route Planner Using Individual Mobility Models0 aTowards a Boosted Route Planner Using Individual Mobility Models  bSpringer Berlin Heidelberg  a108–1231 aGuidotti, Riccardo1 aCintia, Paolo  uhttps://kdd.isti.cnr.it/publications/towards-boosted-route-planner-using-individual-mobility-models00458nas a2200121   4500008004100000245009600041210006900137100002300206700002200229700001700251700002100268856004700289       2015                        eng d00aTowards user-centric data management: individual mobility analytics for collective services0 aTowards usercentric data management individual mobility analytic1 aGuidotti, Riccardo1 aTrasarti, Roberto1 aNanni, Mirco1 aGiannotti, Fosca  uhttp://doi.acm.org/10.1145/2834126.283413201299nas a2200169   4500008004100000245006600041210006600107260003800173300001400211520076600225100002300991700001901014700001801033700002001051700002101071856003701092       2014                        eng d00aRetrieving Points of Interest from Human Systematic Movements0 aRetrieving Points of Interest from Human Systematic Movements  bSpringer International Publishing  a294–3083 aHuman mobility analysis is emerging as a more and more fundamental task to deeply understand human behavior. In the last decade these kind of studies have become feasible thanks to the massive increase in availability of mobility data. A crucial point, for many mobility applications and analysis, is to extract interesting locations for people. In this paper, we propose a novel methodology to retrieve efficiently significant places of interest from movement data. Using car drivers’ systematic movements we mine everyday interesting locations, that is, places around which people life gravitates. The outcomes show the empirical evidence that these places capture nearly the whole mobility even though generated only from systematic movements abstractions.1 aGuidotti, Riccardo1 aMonreale, Anna1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/node/63600325nas a2200085   4500008004100000245007000041210006800111100002300179856003700202       2013                        eng d00aMobility Ranking - Human Mobility Analysis Using Ranking Measures0 aMobility Ranking Human Mobility Analysis Using Ranking Measures1 aGuidotti, Riccardo  uhttps://kdd.isti.cnr.it/node/643