A Collaborative Filtering Recommendation Algorithm for Social Interaction

Abstract

When the traditional collaborative filtering algorithm faces high sparse data, its precision and quality of recommendation become unsatisfied. With the development of social networks, it is possible to selectively fill the missing value in the user-item matrix by using the friendship or trust relationship information of social networks. According to the memory-based collaborative filtering algorithm, in the paper, the two steps which are similarity calculation and user rating prediction are taken into account. Besides, this paper has filled appropriately the missing value and improved memory-based collaborative filtering recommendation algorithms to integrate the social relations. The experiment on the Epinions dataset shows that the improved algorithm can effectively alleviate the sparsity problem of user rating data and perform better than other classic algorithms in RMSE and MAP evaluation metrics.