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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| Published in: | Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 Vol. 3; pp. 547 - 550 |
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| Main Authors: | , , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
Washington, DC, USA
IEEE Computer Society
15.09.2009
IEEE |
| Series: | ACM Conferences |
| Subjects: | |
| ISBN: | 0769538010, 9780769538013 |
| Online Access: | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Carta, Salvatore Boratto, Ludovico Clemente, M. Laura Chessa, Alessandro Agelli, Maurizio |
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| PublicationTitle | Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03 |
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| Snippet | 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... |
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| SubjectTerms | 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 |
| Title | Group Recommendation with Automatic Identification of Users Communities |
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