Group Recommendation with Automatic Identification of Users Communities

Recommender systems usually propose items to single users. However, in some domains like Mobile IPTV or Satellite Systems it might be impossible to generate a program schedule for each user, because of bandwidth limitations. A few approaches were proposed to generate group recommendations. However,...

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Veröffentlicht in:Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 Jg. 3; S. 547 - 550
Hauptverfasser: Boratto, Ludovico, Carta, Salvatore, Chessa, Alessandro, Agelli, Maurizio, Clemente, M. Laura
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Washington, DC, USA IEEE Computer Society 15.09.2009
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
Collaboration
collaborative filtering
Communities
community detection
Conferences
Detection algorithms
Filtering
Human-centered computing > Collaborative and social computing
Information systems > Information retrieval > Evaluation of retrieval results
Information systems > Information retrieval > Retrieval tasks and goals > Document filtering
Information systems > Information retrieval > Retrieval tasks and goals > Information extraction
Information systems > Information retrieval > Users and interactive retrieval > Personalization
Information systems > World Wide Web > Web searching and information discovery > Personalization
Intelligent agent
IPTV
Motion pictures
Partitioning algorithms
Recommender systems
collaborative filtering
community detection
recommender systems
ISBN:0769538010, 9780769538013
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Beschreibung
Zusammenfassung:Recommender systems usually propose items to single users. However, in some domains like Mobile IPTV or Satellite Systems it might be impossible to generate a program schedule for each user, because of bandwidth limitations. A few approaches were proposed to generate group recommendations. However, these approaches take into account that groups of users already exist and no recommender system is able to detect intrinsic users communities. This paper describes an algorithm that detects groups of users whose preferences are similar and predicts recommendations for such groups. Groups of different granularities are generated through a modularity-based Community Detection algorithm, making it possible for a content provider to explore the trade off between the level of personalization of the recommendations and the number of channels. Experimental results show that the quality of group recommendations increases linearly with the number of groups created.
ISBN:0769538010
9780769538013
DOI:10.1109/WI-IAT.2009.346