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Iterative voting under uncertainty for group recommender systems
2010
Proceedings of the fourth ACM conference on Recommender systems - RecSys '10
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
doi:10.1145/1864708.1864763
dblp:conf/recsys/DeryKRS10
fatcat:cufhreheljggtgfthz7jcrh3ai