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Trust-Aware Group Recommendation with Attention Mechanism in Social Network

Due to the fast development of the Internet technology, a variety of social applications have emerged. The users of these social applications are expanding, forming a large-scale social network. In complex and huge social networks, there are a lot of valuable information waiting to be utilized, so that service providers can provide better services for users. On the other hand, to solve the challenge of information overload brought with the communication technology, the recommendation system is becoming more and more important in many fields. In the context of the social network, a recommendation system can better discover the relationship between users and preferences, thus giving a more accurate recommendation. However, most of the traditional recommendation systems mainly focus on providing personalized recommendation service to a single user which are not suitable for group recommendation such as recommendation for a team or a family. In this paper, we proposed a new method based on trust relationship in social network and attention mechanism of deep learning to solve the group recommendation problem. Specifically, we use attention mechanism to capture different group members's preference weight and we consider trust relation among group members when learning group preference in order to capture more information. Through this method, the accuracy of the recommended results can be further improved. The experimental results on Film Trust dataset show that the proposed algorithm in this paper is able to improve the quality of recommendation compared with the existing group recommendation methods.