@inbook {817,
	title = {Going Beyond GDP to Nowcast Well-Being Using Retail Market Data},
	booktitle = {Advances in Network Science},
	year = {2016},
	pages = {29{\textendash}42},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {One 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.},
	doi = {10.1007/978-3-319-28361-6_3},
	author = {Riccardo Guidotti and Michele Coscia and Dino Pedreschi and Diego Pennacchioli}
}
@conference {763,
	title = {Behavioral Entropy and Profitability in Retail},
	booktitle = {IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA{\textquoteright}2015)},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris},
	abstract = {Human 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{\textquoteright}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.},
	author = {Riccardo Guidotti and Michele Coscia and Dino Pedreschi and Diego Pennacchioli}
}
@conference {816,
	title = {Interaction Prediction in Dynamic Networks exploiting Community Discovery},
	booktitle = {International conference on Advances in Social Network Analysis and Mining, ASONAM 2015},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris, France},
	abstract = {Due 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.},
	isbn = {978-1-4503-3854-7},
	doi = {0.1145/2808797.2809401},
	url = {http://dl.acm.org/citation.cfm?doid=2808797.2809401},
	author = {Giulio Rossetti and Riccardo Guidotti and Diego Pennacchioli and Dino Pedreschi and Fosca Giannotti}
}
@article {752,
	title = {Product assortment and customer mobility},
	journal = {EPJ Data Science},
	volume = {4},
	number = {1},
	year = {2015},
	month = {10-2015},
	pages = {1{\textendash}18},
	abstract = {Customers 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{\textquoteright}s size and the customer{\textquoteright}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{\textquoteright}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.},
	doi = {10.1140/epjds/s13688-015-0051-3},
	url = {http://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0051-3},
	author = {Michele Coscia and Diego Pennacchioli and Fosca Giannotti}
}
@conference {827,
	title = {Overlap versus partition: marketing classification and customer profiling in complex networks of products},
	booktitle = {Data engineering workshops (ICDEW), 2014 IEEE 30th international conference on},
	year = {2014},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In 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.},
	doi = {10.1109/ICDEW.2014.6818312},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6818312},
	author = {Diego Pennacchioli and Michele Coscia and Dino Pedreschi}
}
@conference {623,
	title = {The patterns of musical influence on the Last.Fm social network},
	booktitle = {22nd Italian Symposium on Advanced Database Systems, {SEBD} 2014, Sorrento Coast, Italy, June 16-18, 2014.},
	year = {2014},
	author = {Diego Pennacchioli and Giulio Rossetti and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti and Michele Coscia}
}
@article {828,
	title = {The retail market as a complex system},
	journal = {EPJ Data Science},
	volume = {3},
	number = {1},
	year = {2014},
	pages = {1{\textendash}27},
	abstract = {Aim 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{\textquoteright}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{\textquoteright} volumes of sales with the customers{\textquoteright} 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.},
	doi = {10.1140/epjds/s13688-014-0033-x},
	url = {http://link.springer.com/article/10.1140/epjds/s13688-014-0033-x},
	author = {Diego Pennacchioli and Michele Coscia and S Rinzivillo and Fosca Giannotti and Dino Pedreschi}
}
@proceedings {529,
	title = {Explaining the PRoduct Range Effect in Purchase Data},
	year = {2013},
	author = {Diego Pennacchioli and Michele Coscia and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@conference {624,
	title = {The Three Dimensions of Social Prominence},
	booktitle = {Social Informatics - 5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings},
	year = {2013},
	doi = {10.1007/978-3-319-03260-3_28},
	url = {http://dx.doi.org/10.1007/978-3-319-03260-3_28},
	author = {Diego Pennacchioli and Giulio Rossetti and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti and Michele Coscia}
}
@conference {502,
	title = {You Know Because I Know{\textquotedblright}: a Multidimensional Network Approach to Human Resources Problem},
	booktitle = {ASONAM 2013},
	year = {2013},
	author = {Michele Coscia and Giulio Rossetti and Diego Pennacchioli and Damiano Ceccarelli and Fosca Giannotti}
}
