A group recommendation strategy based on user's interaction behavior

With the enrichment of social activities, people are more inclined to group activities in life, study and work. Therefore, how to make a recommendation for group user has become a research hotspot. As the result of the existed recommendation systems are designed for individuals, this paper proposes a group recommendation method based on user's interaction behavior. First, a global trust estimation model based on the interaction between users is proposed, which can be used to estimate the global trust of each user. Second, each user's score of the project can be obtained through collaborative filtering. In the third step, the results obtained in the above two parts are combined to obtain the scoring prediction model proposed in this paper. Finally, according to the characteristics of the group itself, a model GROUP-MAE which is more suitable for evaluating the accuracy of group recommendation systems is proposed in this paper. By comparing experiments on EPINIONS EXTENDED dataset, it is shown that the proposed model is superior to the traditional cooperative fusion strategy in precision and accuracy.