Cross-Domain Item Recommendation Based on User Similarity

Zhenzhen Xu, Huizhen Jiang, Xiangjie Kong, Jialiang Kang, Wei Wang, Feng Xia

Cross-domain recommender systems adopt multiple methods to build relations from source domain to target domain in order to alleviate problems of cold start and sparsity, and improve the performance of recommendations. The majority of traditional methods tend to associate users and items, which neglected the strong influence of friend relation on the recommendation. In this paper, we propose a cross-domain item recommendation model called CRUS based on user similarity, which firstly introduces the trust relation among friends into cross-domain recommendation. Despite friends usually tend to have similar interests in some domains, they share differences either. Considering this, we define all the similar users with the target user as Similar Friends. By modifying the transfer matrix in the random walk, friends sharing similar interests are highlighted. Extensive experiments on Yelp data set show CRUS outperforms the baseline methods on MAE and RMSE.