Dynamic Programming Treatment of the Travelling Salesman Problem
Journal of the ACM (JACM)
More than the sum of its members: challenges for group recommender systems
Proceedings of the working conference on Advanced visual interfaces
Communication complexity of common voting rules
Proceedings of the 6th ACM conference on Electronic commerce
Group recommender systems: a critiquing based approach
Proceedings of the 11th international conference on Intelligent user interfaces
Evaluation of election outcomes under uncertainty
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Managing uncertainty in group recommending processes
User Modeling and User-Adapted Interaction
Uncertainty in preference elicitation and aggregation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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Group Recommendation Systems (GRS) aim at recommending items that are relevant for the joint interest of a group of users. Voting mechanisms assume that users rate all items in order to identify an item that suits the preferences of all group members. This assumption is not feasible in sparse rating scenarios which are common in the recommender systems domain. In this paper we examine an application of voting theory to GRS. We propose a method to accurately determine the winning item while using a minimal set of the group members ratings, assuming that the recommender system has probabilistic knowledge about the distribution of users' ratings of items in the system. Since computing the optimal minimal set of ratings is computationally intractable, we propose two heuristic algorithms that proceed iteratively that aiming atto minimizing the number of required ratings, until identifying a "winning item". Experiments with the Netflix data show that the proposed algorithms reduce the required number of ratings for identifying the "winning item" by more than 50%.