Users' satisfaction in recommendation systems for groups: an approach based on noncooperative games

Authors:
Lucas Augusto Montalvão Costa Carvalho;Hendrik Teixeira Macedo
Affiliations:
Federal University of Sergipe, São Cristóvão, Brazil;Federal University of Sergipe, São Cristóvão, Brazil
Venue:
Proceedings of the 22nd international conference on World Wide Web companion
Year:
2013

Citing 13
Cited 0

Recommender systems

Communications of the ACM
Hybrid Recommender Systems: Survey and Experiments

User Modeling and User-Adapted Interaction
Social Choice Theory and Recommender Systems: Analysis of the Axiomatic Foundations of Collaborative Filtering

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers

User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems

ACM Transactions on Information Systems (TOIS)
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
TV Program Recommendation for Multiple Viewers Based on user Profile Merging

User Modeling and User-Adapted Interaction
PolyLens: a recommender system for groups of users

ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Content-based recommendation systems

The adaptive web
Recommendation to groups

The adaptive web
Approaches to preference elicitation for group recommendation

ICCSA'11 Proceedings of the 2011 international conference on Computational science and Its applications - Volume Part V
Analysis of strategies for building group profiles

UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Evaluation of group profiling strategies

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three

Quantified Score

Hi-index	0.00

Visualization

Abstract

A major difficulty in a recommendation system for groups is to use a group aggregation strategy to ensure, among other things, the maximization of the average satisfaction of group members. This paper presents an approach based on the theory of noncooperative games to solve this problem. While group members can be seen as game players, the items for potential recommendation for the group comprise the set of possible actions. Achieving group satisfaction as a whole becomes, then, a problem of finding the Nash equilibrium. Experiments with a MovieLens dataset and a function of arithmetic mean to compute the prediction of group satisfaction for the generated recommendation have shown statistically significant results when compared to state-of-the-art aggregation strategies, in particular, when evaluation among group members are more heterogeneous. The feasibility of this unique approach is shown by the development of an application for Facebook, which recommends movies to groups of friends.