GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
Experimental Results on Item-Based Algorithms for Independent Domain Collaborative Filtering
AXMEDIS '08 Proceedings of the 2008 International Conference on Automated solutions for Cross Media Content and Multi-channel Distribution
The adaptive web
Enhancing group recommendation by incorporating social relationship interactions
Proceedings of the 16th ACM international conference on Supporting group work
Recommending Flickr groups with social topic model
Information Retrieval
Mining social media: key players, sentiments, and communities
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A group recommender for movies based on content similarity and popularity
Information Processing and Management: an International Journal
Generation of coalition structures to provide proper groups' formation in group recommender systems
Proceedings of the 22nd international conference on World Wide Web companion
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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.