Information filtering via collaborative user clustering modeling

The past few years have witnessed the great success of recommender systems, which can significantly help users to find out personalized items for them from the information era. One of the widest applied recommendation methods is the Matrix Factorization (MF). However, most of the researches on this...

Full description

Saved in:
Bibliographic Details
Published in:Physica A 2014-02, Vol.396, p.195-203
Main Authors: Zhang, Chu-Xu, Zhang, Zi-Ke, Yu, Lu, Liu, Chuang, Liu, Hao, Yan, Xiao-Yong
Format: Article
Language:English
Subjects:
Clustering
Collaborative filtering
Factorization
Filtering
Filtration
Matrix factorization
Personalized
Recommender systems
Regularization
Statistical mechanics
User clustering regularization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The past few years have witnessed the great success of recommender systems, which can significantly help users to find out personalized items for them from the information era. One of the widest applied recommendation methods is the Matrix Factorization (MF). However, most of the 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 the user information. In addition, we compared the proposed model with three typical other methods: User-Mean (UM), Item-Mean (IM) and standard MF. Experimental results on two real-world datasets, MovieLens 1M and MovieLens 100k, show that our method performs better than other three methods in the accuracy of recommendation. •Propose a user behavior model to cluster users and improve recommendation.•Optimize the results by integrating the user clustering regularization term based on genres.•Experimental results show that our method performs better than two other baseline methods.
ISSN:0378-4371
1873-2119
DOI:10.1016/j.physa.2013.11.024