A Novel Social-Choice Strategy for Group Modeling in Recommender Systems

Personalized recommender systems are usually designed to provide recommendations adapted to the preferences of a single user. Group recommender systems on the other hand suggest items to a group by combining individual models into a group model. This group model allows to merge the preferences of the individual members of a group and thereby derive a group preference for each item by using different group modeling strategies. In this paper we first critically analyse various group modeling strategies as outlined in the literature and point out the limitations of each of them. Thereafter we propose a group modeling technique called Most Members Merry (MMM) strategy to address those limitations. Our experimental results show that MMMF exhibits high performance in terms of precision and recall as compared to the existing strategies.