Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
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
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
Using Context to Improve Predictive Modeling of Customers in Personalization Applications
IEEE Transactions on Knowledge and Data Engineering
FlexRecs: expressing and combining flexible recommendations
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Group recommendation: semantics and efficiency
Proceedings of the VLDB Endowment
The adaptive web
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Mining of Massive Datasets
gRecs: a group recommendation system based on user clustering
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
A group recommendation system for online communities
International Journal of Information Management: The Journal for Information Professionals
Contextual recommendations for groups
ER'12 Proceedings of the 2012 international conference on Advances in Conceptual Modeling
| Hi-index | 0.00 |
Recommendation systems have received significant attention, with most of the proposed methods focusing on personal recommendations. However, there are contexts in which the items to be suggested are not intended for a single user but for a group of people. For example, assume a group of friends or a family that is planning to watch a movie or visit a restaurant. In this paper, we propose an extensive model for group recommendations that exploits recommendations for items that similar users to the group members liked in the past. We do not exhaustively search for similar users in the whole user base, but we pre-partition users into clusters of similar ones and use the cluster members for recommendations. We efficiently aggregate the single user recommendations into group recommendations by leveraging the power of a top-k algorithm. We evaluate our approach in a real dataset of movie ratings.