Group Recommendation Using Collaborative Filtering

Collaborative filtering is one of the most widely used techniques in building recommender systems for individual users, on the other hand, many group recommenders have been build due to the fact that many daily activities are group activities. In this paper, we study the technique which collaborative filtering used to predict users' preference for unknown items base on their neighbors' preference; and the various preference aggregation strategies used in prior group recommendation systems. The goal of this paper is to examine the relationship between various aggregation strategies and the size of the group in terms of maximizing the global satisfaction of the group.