Abstract
With the exponential growth of the online community activities, group recommender systems have become popular in recent years. However, making recommendations relevant to the common interests of a group is a challenging task due to the diversity of group members’ preferences. In this paper, we propose a novel Trust-aware Group Recommendation (TGR) approach to improve the performance of group recommendations. TGR uses a new group trust metric that is optimized by Particle Swarm Optimization (PSO). This metric directly provides a set of neighbors for a group of users. The experimental results show that TGR can improve the accuracy and run-time performance of other group recommender systems.
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