Recommender systems help online users find relevant content by suggesting information of potential interest to them [4]. Social recommender is any recommender with online social relations as an additional input, namely, augmenting an existing recommendation engine with additional social content [5]. In this talk we describe our experience and lessons learned in developing social recommender systems able to deliver attractive and relevant content. More specifically, we focus on machine learning and data mining techniques exploited for the following goals: (i) to extract user preferences and needs to be used in the information filtering process; (ii) to harness the vast amount of information from user reviews, social networking, and local search Web sites; (iii) to infer peculiar users’ attitudes (i.e., sentiments, opinions, and ways of thinking) toward their own interests [3]; (iv) to define the context of use in the recommendation process [1]. Achieving the above goals allowed us to realize a social recommender system for context-aware mobile services [2], which provide users with personalized recommendations about points of interest (e.g., restaurants or cultural events) in the surroundings of the user’s current position.

Biancalana, C., FELTONI GURINI, D., Gasparetti, F., Micarelli, A., Sansonetti, G. (2016). Machine Learning and Data Mining Techniques for Efficient Social Recommender Systems. In Proceedings of MLDM.it 2016.

Machine Learning and Data Mining Techniques for Efficient Social Recommender Systems

Claudio Biancalana;Davide Feltoni Gurini;Fabio Gasparetti;Alessandro Micarelli;Giuseppe Sansonetti
2016

Abstract

Recommender systems help online users find relevant content by suggesting information of potential interest to them [4]. Social recommender is any recommender with online social relations as an additional input, namely, augmenting an existing recommendation engine with additional social content [5]. In this talk we describe our experience and lessons learned in developing social recommender systems able to deliver attractive and relevant content. More specifically, we focus on machine learning and data mining techniques exploited for the following goals: (i) to extract user preferences and needs to be used in the information filtering process; (ii) to harness the vast amount of information from user reviews, social networking, and local search Web sites; (iii) to infer peculiar users’ attitudes (i.e., sentiments, opinions, and ways of thinking) toward their own interests [3]; (iv) to define the context of use in the recommendation process [1]. Achieving the above goals allowed us to realize a social recommender system for context-aware mobile services [2], which provide users with personalized recommendations about points of interest (e.g., restaurants or cultural events) in the surroundings of the user’s current position.
Biancalana, C., FELTONI GURINI, D., Gasparetti, F., Micarelli, A., Sansonetti, G. (2016). Machine Learning and Data Mining Techniques for Efficient Social Recommender Systems. In Proceedings of MLDM.it 2016.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/394113
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