Recommendation by Mining Multiple User Behaviors with Group Sparsity

@inproceedings{Yuan2014RecommendationBM,
  title={Recommendation by Mining Multiple User Behaviors with Group Sparsity},
  author={Ting Yuan and Jian Cheng and Xi Zhang and Shuang Qiu and Hanqing Lu},
  booktitle={AAAI},
  year={2014}
}
Recently, some recommendation methods try to improve the prediction results by integrating information from user’s multiple types of behaviors. How to model the dependence and independence between different behaviors is critical for them. In this paper, we propose a novel recommendation model, the Group-Sparse Matrix Factorization (GSMF), which factorizes the rating matrices for multiple behaviors into the user and item latent factor space with group sparsity regularization. It can (1) select… CONTINUE READING
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