Multi-Feature Discrete Collaborative Filtering for Fast Cold-Start Recommendation


  • Yang Xu Shandong Normal University
  • Lei Zhu Shandong Normal University
  • Zhiyong Cheng Shandong Computer Science Center
  • Jingjing Li University of Electronic Science and Technology of China
  • Jiande Sun Shandong Normal University



Hashing is an effective technique to address the large-scale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items. However, existing hashing-based recommendation methods still suffer from two important problems: 1) Their recommendation process mainly relies on the user-item interactions and single specific content feature. When the interaction history or the content feature is unavailable (the cold-start problem), their performance will be seriously deteriorated. 2) Existing methods learn the hash codes with relaxed optimization or adopt discrete coordinate descent to directly solve binary hash codes, which results in significant quantization loss or consumes considerable computation time. In this paper, we propose a fast cold-start recommendation method, called Multi-Feature Discrete Collaborative Filtering (MFDCF), to solve these problems. Specifically, a low-rank self-weighted multi-feature fusion module is designed to adaptively project the multiple content features into binary yet informative hash codes by fully exploiting their complementarity. Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations. Experiments on two public recommendation datasets demonstrate that MFDCF outperforms the state-of-the-arts on various aspects.




How to Cite

Xu, Y., Zhu, L., Cheng, Z., Li, J., & Sun, J. (2020). Multi-Feature Discrete Collaborative Filtering for Fast Cold-Start Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 270-278.



AAAI Technical Track: AI and the Web