01694nas 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-data01914nas 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-retail01436nas 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=2808797.280940102010nas a2200157   4500008004100000245004500041210004500086260001200131300001100143490000600154520154300160100002001703700002401723700002101747856008401768       2015                        eng d00aProduct assortment and customer mobility0 aProduct assortment and customer mobility  c10-2015  a1–180 v43 aCustomers mobility is dependent on the sophistication of their needs: sophisticated customers need to travel more to fulfill their needs. In this paper, we provide more detailed evidence of this phenomenon, providing an empirical validation of the Central Place Theory. For each customer, we detect what is her favorite shop, where she purchases most products. We can study the relationship between the favorite shop and the closest one, by recording the influence of the shop’s size and the customer’s sophistication in the discordance cases, i.e. the cases in which the favorite shop is not the closest one. We show that larger shops are able to retain most of their closest customers and they are able to catch large portions of customers from smaller shops around them. We connect this observation with the shop’s larger sophistication, and not with its other characteristics, as the phenomenon is especially noticeable when customers want to satisfy their sophisticated needs. This is a confirmation of the recent extensions of the Central Place Theory, where the original assumptions of homogeneity in customer purchase power and needs are challenged. Different types of shops have also different survival logics. The largest shops get closed if they are unable to catch customers from the smaller shops, while medium size shops get closed if they cannot retain their closest customers. All analysis are performed on a large real-world dataset recording all purchases from millions of customers across the west coast of Italy.1 aCoscia, Michele1 aPennacchioli, Diego1 aGiannotti, Fosca  uhttp://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0051-301798nas a2200133   4500008004100000245011000041210006900151260000900220520130600229100002401535700002001559700002001579856006501599       2014                        eng d00aOverlap versus partition: marketing classification and customer profiling in complex networks of products0 aOverlap versus partition marketing classification and customer p  bIEEE3 aIn recent years we witnessed the explosion in the availability of data regarding human and customer behavior in the market. This data richness era has fostered the development of useful applications in understanding how markets and the minds of the customers work. In this paper we focus on the analysis of complex networks based on customer behavior. Complex network analysis has provided a new and wide toolbox for the classic data mining task of clustering. With community discovery, i.e. the detection of functional modules in complex networks, we are now able to group together customers and products using a variety of different criteria. The aim of this paper is to explore this new analytic degree of freedom. We are interested in providing a case study uncovering the meaning of different community discovery algorithms on a network of products connected together because co-purchased by the same customers. We focus our interest in the different interpretation of a partition approach, where each product belongs to a single community, against an overlapping approach, where each product can belong to multiple communities. We found that the former is useful to improve the marketing classification of products, while the latter is able to create a collection of different customer profiles.1 aPennacchioli, Diego1 aCoscia, Michele1 aPedreschi, Dino  uhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=681831200482nas a2200145   4500008004100000245006800041210006300109100002400172700002100196700002100217700002000238700002100258700002000279856003700299       2014                        eng d00aThe patterns of musical influence on the Last.Fm social network0 apatterns of musical influence on the LastFm social network1 aPennacchioli, Diego1 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aCoscia, Michele  uhttps://kdd.isti.cnr.it/node/62302340nas a2200169   4500008004100000245004200041210003800083300001100121490000600132520186000138100002401998700002002022700001802042700002102060700002002081856006902101       2014                        eng d00aThe retail market as a complex system0 aretail market as a complex system  a1–270 v33 aAim of this paper is to introduce the complex system perspective into retail market analysis. Currently, to understand the retail market means to search for local patterns at the micro level, involving the segmentation, separation and profiling of diverse groups of consumers. In other contexts, however, markets are modelled as complex systems. Such strategy is able to uncover emerging regularities and patterns that make markets more predictable, e.g. enabling to predict how much a country’s GDP will grow. Rather than isolate actors in homogeneous groups, this strategy requires to consider the system as a whole, as the emerging pattern can be detected only as a result of the interaction between its self-organizing parts. This assumption holds also in the retail market: each customer can be seen as an independent unit maximizing its own utility function. As a consequence, the global behaviour of the retail market naturally emerges, enabling a novel description of its properties, complementary to the local pattern approach. Such task demands for a data-driven empirical framework. In this paper, we analyse a unique transaction database, recording the micro-purchases of a million customers observed for several years in the stores of a national supermarket chain. We show the emergence of the fundamental pattern of this complex system, connecting the products’ volumes of sales with the customers’ volumes of purchases. This pattern has a number of applications. We provide three of them. By enabling us to evaluate the sophistication of needs that a customer has and a product satisfies, this pattern has been applied to the task of uncovering the hierarchy of needs of the customers, providing a hint about what is the next product a customer could be interested in buying and predicting in which shop she is likely to go to buy it.1 aPennacchioli, Diego1 aCoscia, Michele1 aRinzivillo, S1 aGiannotti, Fosca1 aPedreschi, Dino  uhttp://link.springer.com/article/10.1140/epjds/s13688-014-0033-x00477nas a2200133   4500008003900000245005700039210005700096100002400153700002000177700001800197700002000215700002100235856008700256       2013                          d00aExplaining the PRoduct Range Effect in Purchase Data0 aExplaining the PRoduct Range Effect in Purchase Data1 aPennacchioli, Diego1 aCoscia, Michele1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/explaining-product-range-effect-purchase-data00453nas a2200145   4500008004100000245004600041210004200087100002400129700002100153700002100174700002000195700002100215700002000236856005100256       2013                        eng d00aThe Three Dimensions of Social Prominence0 aThree Dimensions of Social Prominence1 aPennacchioli, Diego1 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aCoscia, Michele  uhttp://dx.doi.org/10.1007/978-3-319-03260-3_2800579nas a2200133   4500008003900000245009500039210006900134100002000203700002100223700002400244700002400268700002100292856013200313       2013                          d00aYou Know Because I Know”: a Multidimensional Network Approach to Human Resources Problem0 aYou Know Because I Know a Multidimensional Network Approach to H1 aCoscia, Michele1 aRossetti, Giulio1 aPennacchioli, Diego1 aCeccarelli, Damiano1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/you-know-because-i-know%E2%80%9D-multidimensional-network-approach-human-resources-problem