Information Filtering via Collaborative User Clustering Modeling

TL;DR

This paper enhances matrix factorization for recommender systems by incorporating user clustering to better capture user interests, leading to improved recommendation accuracy on real-world data.

Contribution

It introduces a novel matrix factorization model with user clustering regularization, integrating user interest information for more accurate recommendations.

Findings

Outperforms User-Mean, Item-Mean, and standard MF in accuracy

Demonstrates effectiveness on MovieLens dataset

Improves recommendation quality by considering user interest

Abstract

The past few years have witnessed the great success of recommender systems, which can significantly help users find out personalized items for them from the information era. One of the most widely applied recommendation methods is the Matrix Factorization (MF). However, most of researches on this topic have focused on mining the direct relationships between users and items. In this paper, we optimize the standard MF by integrating the user clustering regularization term. Our model considers not only the user-item rating information, but also takes into account the user interest. We compared the proposed model with three typical other methods: User-Mean (UM), Item-Mean (IM) and standard MF. Experimental results on a real-world dataset, \emph{MovieLens}, show that our method performs much better than other three methods in the accuracy of recommendation.