Improving Collaborative Filtering via Hidden Structured Constraint

Qing Zhang, Houfeng Wang

Abstract: Matrix factorization models, as one of the most powerful Collaborative Filtering approaches, have greatly advanced the recommendation tasks. However, few of them are able to explicitly consider structured constraint for modeling user interests. To solve this problem, we propose a novel matrix factorization model with adaptive graph regularization framework, which can automatically discover latent user communities jointly with learning latent user representations, to enhance the discriminative power for recommendation. Experiments on real-world datasets demonstrate the effectiveness of the proposed method.