2018/01/01 by Zhipeng Wu, Hui Tian, Xuzhen Zhu +1 · 20 citations
Computer Science · Mathematics · #Advanced Graph Neural Networks #Algorithm #Artificial intelligence #Centrality #Computer science #Data mining #Factorization #Image Retrieval and Classification Techniques #Information retrieval #Machine learning #Mathematics #Matrix decomposition #Preference #Recommender Systems and Techniques #Recommender system #Reliability (semiconductor) #Statistics #cs.IR
paper · pdf · doi:10.1007/978-3-319-93803-5_11
published in Lecture notes in computer science, 114-125 (Springer Science+Business Media)
openalex publication_date 2018/01/01 · arxiv created 2018/06/20 · arxiv updated 2018/06/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.