Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
IEEE Transactions on Knowledge and Data Engineering
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations
Group Recommendation with Automatic Identification of Users Communities
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
The adaptive web
Characterization, Stability and Convergence of Hierarchical Clustering Methods
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using personality to create alliances in group recommender systems
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
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Group recommender systems usually provide recommendations to a fixed and predetermined set of members. In some situations, however, there is a set of people (N) that should be organized into smaller and cohesive groups, so it is possible to provide more effective recommendations to each of them. This is not a trivial task. In this paper we propose an innovative approach for grouping people within the recommendation problem context. The problem is modeled as a coalitional game from Game Theory. The goal is to group people into exhaustive and disjoint coalitions so as to maximize the social welfare function of the group. The optimal coalition structure is that with highest summation over all social welfare values. Similarities between recommendation system users are used to define the social welfare function. We compare our approach with K-Means clustering for a dataset from Movielens. Results have shown that the proposed approach performs better than K-Means for both average group satisfaction and Davies-Bouldin index metrics when the number of coalitions found is not greater than 4 (K N = 12).