基于共享评级迁移的跨域推荐算法.

Data sparsity and user cold start are always the problems to be solved in the recommendation system. This paper proposed a cross domain recommendation algorithm SRTCD. Considering the similarity of latent factors of user groups / items in different domains, the algorithm decomposed the rating matrix of each domain into probability matrix to obtain the latent factors of users and items. Then, it used K-means algorithm based on simulated annealing and genetic algorithm to cluster users and items respectively, and used the inner product of user category and items category as shared rating. Then, it obtained the recommendation results by using the shared rating of each domain dataset and the specific rating of the target domain dataset. Finally, it verified the proposed method by public data sets, and the results show that the performance of SRTCD is significantly better than that of common recommendation algorithms. [ABSTRACT FROM AUTHOR]