Group Recommender Model Based on Preference Interaction

Abstract

With the application of recommender system increasing, the research and application of group recommender have been paid more attention. In the course of group activities, the unknown preferences of users are often affected by other members of the group. However, in the existing group recommender system, this effect is not taken into account. In this paper, we propose a novel recommender model that incorporates the preference interaction in the group recommender into rating predicting process. The model is divided into two parts: self-prediction and preference-interaction, the preference-interaction will be systematically analyzed and illustrated. For every user in the group, we use group activity history information and recommender post-rating feedback mechanism to generate personalized interactive parameters. Thus, it can improve the group’s recommender accuracy. Finally, the model is combined with the collaborative filtering algorithm and compared with the algorithm without the model on the MovieLens dataset. The experiment results show that the model proposed in this paper can improve the accuracy of the group recommender results obviously.