Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality
This work addresses the issue of inconsistent rating reliability for users and developers in recommender systems, but it is incremental as it builds upon existing matrix factorization methods.
The paper tackles the problem of unreliable explicit ratings in recommender systems by analyzing rating deviations and introducing user-based and item-based rating centrality to measure reliability, resulting in an optimized matrix factorization algorithm that shows better performance, particularly on sparse datasets.
Matrix factorization (MF) is extensively used to mine the user preference from explicit ratings in recommender systems. However, the reliability of explicit ratings is not always consistent, because many factors may affect the user's final evaluation on an item, including commercial advertising and a friend's recommendation. Therefore, mining the reliable ratings of user is critical to further improve the performance of the recommender system. In this work, we analyze the deviation degree of each rating in overall rating distribution of user and item, and propose the notion of user-based rating centrality and item-based rating centrality, respectively. Moreover, based on the rating centrality, we measure the reliability of each user rating and provide an optimized matrix factorization recommendation algorithm. Experimental results on two popular recommendation datasets reveal that our method gets better performance compared with other matrix factorization recommendation algorithms, especially on sparse datasets.