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Matrix factorization (MF) is extensively used to mine the user preferencefrom explicit ratings in recommender systems. However, the reliability ofexplicit ratings is not always consistent, because many factors may affect theuser's final evaluation on an item, including commercial advertising and afriend's recommendation. Therefore, mining the reliable ratings of user iscritical to further improve the performance of the recommender system. In thiswork, we analyze the deviation degree of each rating in overall ratingdistribution of user and item, and propose the notion of user-based ratingcentrality and item-based rating centrality, respectively. Moreover, based onthe rating centrality, we measure the reliability of each user rating andprovide an optimized matrix factorization recommendation algorithm.Experimental results on two popular recommendation datasets reveal that ourmethod gets better performance compared with other matrix factorizationrecommendation algorithms, especially on sparse datasets.

Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality