Sciweavers

Share
RECSYS
2015
ACM

Overlapping Community Regularization for Rating Prediction in Social Recommender Systems

4 years 2 months ago
Overlapping Community Regularization for Rating Prediction in Social Recommender Systems
Recommender systems have become de facto tools for suggesting items that are of potential interest to users. Predicting a user’s rating on an item is the fundamental recommendation task. Traditional methods that generate predictions by analyzing the user-item rating matrix perform poorly when the matrix is sparse. Recent approaches use data from social networks to improve accuracy. However, most of the social-network based recommender systems only consider direct friendships and they are less effective when the targeted user has few social connections. In this paper, we propose two alternative models that incorporate the overlapping community regularization into the matrix factorization framework. Our empirical study on four real datasets shows that our approaches outperform the state-of-the-art algorithms in both traditional and social-network based recommender systems regarding both coldstart users and normal users.
Hui Li, Dingming Wu, Wenbin Tang, Nikos Mamoulis
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where RECSYS
Authors Hui Li, Dingming Wu, Wenbin Tang, Nikos Mamoulis
Comments (0)
books