A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems

Authors

  • Xin Dong Ctrip Travel Network Technology (Shanghai) Co., Limited.
  • Lei Yu Ctrip Travel Network Technology (Shanghai) Co., Limited.
  • Zhonghuo Wu Ctrip Travel Network Technology (Shanghai) Co., Limited.
  • Yuxia Sun Ctrip Travel Network Technology (Shanghai) Co., Limited.
  • Lingfeng Yuan Ctrip Travel Network Technology (Shanghai) Co., Limited.
  • Fangxi Zhang Ctrip Travel Network Technology (Shanghai) Co., Limited.

DOI:

https://doi.org/10.1609/aaai.v31i1.10747

Keywords:

Recommender System, Collaborative Filtering, Deep Learning

Abstract

Collaborative filtering (CF) is a widely used approach in recommender systems to solve many real-world problems. Traditional CF-based methods employ the user-item matrix which encodes the individual preferences of users for items for learning to make recommendation. In real applications, the rating matrix is usually very sparse, causing CF-based methods to degrade significantly in recommendation performance. In this case, some improved CF methods utilize the increasing amount of side information to address the data sparsity problem as well as the cold start problem. However, the learned latent factors may not be effective due to the sparse nature of the user-item matrix and the side information. To address this problem, we utilize advances of learning effective representations in deep learning, and propose a hybrid model which jointly performs deep users and items’ latent factors learning from side information and collaborative filtering from the rating matrix. Extensive experimental results on three real-world datasets show that our hybrid model outperforms other methods in effectively utilizing side information and achieves performance improvement.

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Published

2017-02-12

How to Cite

Dong, X., Yu, L., Wu, Z., Sun, Y., Yuan, L., & Zhang, F. (2017). A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10747

Issue

Section

Main Track: Machine Learning Applications