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2016
Report
Open Access
ISTI young research award 2016
Barsocchi P., Candela L., Ciancia V., Dellepiane M., Esuli A., Girardi M., Girolami M., Guidotti R., Lonetti F., Malomo L., Moroni D., Nardini F. M., Palumbo F., Pappalardo L., Pascali M. A., Rinzivillo S.
The ISTI Young Researcher Award is an award for young people of Institute of Information Science and Technologies (ISTI) with high scientific production. In particular, the award is granted to young staff members (less than 35 years old) by assessing the yearly scientific production of the year preceding the award. This report documents procedure and results of the 2016 edition of the award.Source: ISTI Technical reports, 2016
Barsocchi P., Candela L., Ciancia V., Dellepiane M., Esuli A., Girardi M., Girolami M., Guidotti R., Lonetti F., Malomo L., Moroni D., Nardini F. M., Palumbo F., Pappalardo L., Pascali M. A., Rinzivillo S.
The ISTI Young Researcher Award is an award for young people of Institute of Information Science and Technologies (ISTI) with high scientific production. In particular, the award is granted to young staff members (less than 35 years old) by assessing the yearly scientific production of the year preceding the award. This report documents procedure and results of the 2016 edition of the award.Source: ISTI Technical reports, 2016
See at:
ISTI Repository 
| CNR ExploRA
2016
Conference article
Open Access
"Are we playing like Music-Stars?" Placing emerging artists on the Italian music scene
Pollacci L., Guidotti R., Rossetti G.
The Italian emerging bands chase success on the footprint of popular artists by playing rhythmic danceable and happy songs. Our finding comes out from a study of the Italian music scene and how the new generation of musicians relate with the tradition of their country. By analyzing Spotify data we investigated the peculiarity of regional music and we placed emerging bands within the musical movements defined by already successful artists. The approach proposed and the results obtained are a first attempt to outline rules suggesting the importance of those features needed to increase popularity in the Italian music scene.Source: 9th International Workshop on Machine Learning and Music, pp. 51–55, Riva del Garda, Italy, 23 September 2016
Pollacci L., Guidotti R., Rossetti G.
The Italian emerging bands chase success on the footprint of popular artists by playing rhythmic danceable and happy songs. Our finding comes out from a study of the Italian music scene and how the new generation of musicians relate with the tradition of their country. By analyzing Spotify data we investigated the peculiarity of regional music and we placed emerging bands within the musical movements defined by already successful artists. The approach proposed and the results obtained are a first attempt to outline rules suggesting the importance of those features needed to increase popularity in the Italian music scene.Source: 9th International Workshop on Machine Learning and Music, pp. 51–55, Riva del Garda, Italy, 23 September 2016
See at:
sites.google.com | CNR ExploRA
2016
Conference article
Open Access
Where is my next friend? Recommending enjoyable profiles in location based services
Guidotti R., Berlingerio M.
How 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 10km of their two most visited locations. Our results are encouraging, and open the way for a new class of recommender systems based on enjoyability.Source: CompleNet 2016 - Complex Networks VII. 7th Workshop on Complex Networks, pp. 65–78, Dijion, France, 23-25 March, 2016
DOI: 10.1007/978-3-319-30569-1_5
Project(s): PETRA
Metrics:
Guidotti R., Berlingerio M.
How 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 10km of their two most visited locations. Our results are encouraging, and open the way for a new class of recommender systems based on enjoyability.Source: CompleNet 2016 - Complex Networks VII. 7th Workshop on Complex Networks, pp. 65–78, Dijion, France, 23-25 March, 2016
DOI: 10.1007/978-3-319-30569-1_5
Project(s): PETRA
Metrics:
See at:
www.springer.com | doi.org 
| link.springer.com | CNR ExploRA
2016
Conference article
Open Access
Going beyond GDP to nowcast Well-Being using retail market data
Guidotti R., Coscia M., Pedreschi D., Pennacchioli D.
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.Source: NetSci-X 2016 - Advances in Network Science. 12th International Conference and School, pp. 29–42, Wroclaw, Poland, 11-13 January 2016
DOI: 10.1007/978-3-319-28361-6_3
Project(s): CIMPLEX, SoBigData
Metrics:
Guidotti R., Coscia M., Pedreschi D., Pennacchioli D.
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.Source: NetSci-X 2016 - Advances in Network Science. 12th International Conference and School, pp. 29–42, Wroclaw, Poland, 11-13 January 2016
DOI: 10.1007/978-3-319-28361-6_3
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
arpi.unipi.it | Lecture Notes in Computer Science | link.springer.com | CNR ExploRA
2016
Contribution to book
Restricted
Audio ergo sum. A personal data model for musical preferences
Guidotti R., Rossetti G., Pedreschi D.
Nobody 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.Source: Software Technologies: Applications and Foundations, edited by Milazzo, Paolo; Varró, Dániel; Wimmer, Manuel, pp. 51–66, 2016
DOI: 10.1007/978-3-319-50230-4_5
Project(s): CIMPLEX
Metrics:
Guidotti R., Rossetti G., Pedreschi D.
Nobody 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.Source: Software Technologies: Applications and Foundations, edited by Milazzo, Paolo; Varró, Dániel; Wimmer, Manuel, pp. 51–66, 2016
DOI: 10.1007/978-3-319-50230-4_5
Project(s): CIMPLEX
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2016
Journal article
Open Access
Unveiling mobility complexity through complex network analysis
Guidotti R., Monreale A., Rinzivillo S., Pedreschi D., Giannotti F.
The 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.Source: Social Network Analysis and Mining 6 (2016). doi:10.1007/s13278-016-0369-2
DOI: 10.1007/s13278-016-0369-2
Project(s): CIMPLEX, PETRA
Metrics:
Guidotti R., Monreale A., Rinzivillo S., Pedreschi D., Giannotti F.
The 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.Source: Social Network Analysis and Mining 6 (2016). doi:10.1007/s13278-016-0369-2
DOI: 10.1007/s13278-016-0369-2
Project(s): CIMPLEX
, PETRA Metrics:
See at:
Social Network Analysis and Mining | link.springer.com | Social Network Analysis and Mining | ISTI Repository | CNR ExploRA
2016
Contribution to book
Restricted
ICON loop carpooling show case
Nanni M., Kotthoff L., Guidotti R., Òsullivan B., Pedreschi D.
In 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.Source: Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach, edited by Bessiere, C.; De Raedt, L.; Kotthoff, L.; Nijssen, S.; O'Sullivan, B.; Pedreschi, D., pp. 310–324, 2016
DOI: 10.1007/978-3-319-50137-6_13
Project(s): PETRA
Metrics:
Nanni M., Kotthoff L., Guidotti R., Òsullivan B., Pedreschi D.
In 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.Source: Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach, edited by Bessiere, C.; De Raedt, L.; Kotthoff, L.; Nijssen, S.; O'Sullivan, B.; Pedreschi, D., pp. 310–324, 2016
DOI: 10.1007/978-3-319-50137-6_13
Project(s): PETRA
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA