Optimization Matrix Factorization Recommendation Algorithm Based on Rating Centrality

TL;DR

This paper introduces a matrix factorization recommendation algorithm that incorporates rating centrality to assess rating reliability, improving recommendation accuracy especially in sparse datasets.

Contribution

It proposes a novel rating centrality measure to evaluate rating reliability and integrates it into matrix factorization for enhanced recommendations.

Findings

Outperforms existing MF algorithms on benchmark datasets.

Improves recommendation accuracy in sparse data scenarios.

Effectively assesses rating reliability using rating centrality.

Abstract

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.