User recommendation method based on joint probability matrix decomposition in CPS networks

Abstract In recent years, with the rapid development of the Internet, various virtual communities continue to emerge, and the phenomenon of user groups working together is gradually increasing. People begin to pay more attention to group-oriented recommendation. Most of existing group recommendation methods are improved on the memory-based collaborative filtering recommendation method, or think that the members of the group are independent of each other, ignoring the impact of association among the members of the group on the results of group recommendation. In this paper, a group recommendation method based on joint probability matrix decomposition is proposed to better model the group recommendation problem. Firstly, the user-plus-person group information is used to calculate the correlation between users. Secondly, the user correlation matrix is integrated into the process of probability matrix decomposition to get the individual prediction score. Finally, the group-to-item prediction score is obtained by using the common synthesis strategy in group-oriented recommendation problem. Furthermore, the proposed method is compared with existing group recommendation methods. Experiments on CiteULike dataset show that the proposed method achieves better recommendation results in accuracy, recall and other evaluation indicators.

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