Research on the Integration Strategy of Group Recommendation Based on User's Interactive Behaviors

In order to reduce the unsatisfictory among the group and improve the accuracy of group recommender system, this paper proposes a preference integration strategy based on users' interactive behaviors.This model obtained the each members' prediction rating on the items and group's predictions on the items based on the individual and group collaborative filtering algorithm. Additionally, this paper puts forward a method that obtains the weights of members, then obtains the final prediction of items through the preference fusion method. The model was evaluated and verified by the improved GMAE. The results of the experiments show that the proposed algorithm is better than traditional GCF on accuracy and diversity.