Leveraging tagging and rating for recommendation: RMF meets weighted diffusion on tripartite graphs

TL;DR

This paper introduces WUDiff_RMF, a hybrid recommendation model that combines matrix factorization with a weighted user-diffusion algorithm on tripartite graphs, effectively addressing data sparsity and improving accuracy.

Contribution

It proposes a novel hybrid model integrating weighted user-diffusion with RMF, specifically designed to handle tag sparsity in social tagging systems for better recommendations.

Findings

Outperforms existing methods in recommendation accuracy.

Reduces impact of data sparsity, especially with few user ratings and tags.

Effective on multiple real-world datasets.

Abstract

Recommender systems (RSs) have been a widely exploited approach to solving the information overload problem. However, the performance is still limited due to the extreme sparsity of the rating data. With the popularity of Web 2.0, the social tagging system provides more external information to improve recommendation accuracy. Although some existing approaches combine the matrix factorization models with co-occurrence properties and context of tags, they neglect the issue of tag sparsity without the commonly associated tags problem that would also result in inaccurate recommendations. Consequently, in this paper, we propose a novel hybrid collaborative filtering model named WUDiff_RMF, which improves Regularized Matrix Factorization (RMF) model by integrating Weighted User-Diffusion-based CF algorithm(WUDiff) that obtains the information of similar users from the weighted tripartite user-item-tag graph. This model aims to capture the degree correlation of the user-item-tag tripartite network to enhance the performance of recommendation. Experiments conducted on four real-world datasets demonstrate that our approach significantly performs better than already widely used methods in the accuracy of recommendation. Moreover, results show that WUDiff_RMF can alleviate the data sparsity, especially in the circumstance that users have made few ratings and few tags.