MusicFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
TV Program Recommendation for Multiple Viewers Based on user Profile Merging
User Modeling and User-Adapted Interaction
Fast Random Walk with Restart and Its Applications
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Group recommendation: semantics and efficiency
Proceedings of the VLDB Endowment
Group-based recipe recommendations: analysis of data aggregation strategies
Proceedings of the fourth ACM conference on Recommender systems
Group recommendations with rank aggregation and collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Enhancing group recommendation by incorporating social relationship interactions
Proceedings of the 16th ACM international conference on Supporting group work
Commenders: A recommendation procedure for online book communities
Electronic Commerce Research and Applications
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Analysis of strategies for building group profiles
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
TV program recommendation for groups based on muldimensional TV-anytime classifications
IEEE Transactions on Consumer Electronics
Utilisabilité d'un Espace Personnel d'Information Modifiable par les Utilisateurs
Proceedings of the 25ième conférence francophone on l'Interaction Homme-Machine
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With the rapid popularity of smart devices, users are easily and conveniently accessing rich multimedia content. Consequentially, the increasing need for recommender services, from both individual users and groups of users, has arisen. In this paper, we present a graph-based approach to a recommender system that can make recommendations most notably to groups of users. From rating information, we first model a signed graph that contains both positive and negative links between users and items. On this graph we examine two distinct random walks to separately quantify the degree to which a group of users would like or dislike items. We then employ a differential ranking approach for tailoring recommendations to the group. Our empirical evaluations on the MovieLens dataset demonstrate that the proposed group recommendation method performs better than existing alternatives. We also demonstrate the feasibility of Folkommender for smartphones.