Learning binary codes with neural collaborative filtering for efficient recommendation systems

Yang Li, Suhang Wang, Quan Pan, Haiyun Peng, Tao Yang, Erik Cambria

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


The fast-growing e-commerce scenario brings new challenges to traditional collaborative filtering because the huge amount of users and items requires large storage and efficient recommendation systems. Hence, hashing for collaborative filtering has attracted increasing attention as binary codes can significantly reduce the storage requirement and make similarity calculations efficient. In this paper, we investigate the novel problem of deep collaborative hashing codes on user–item ratings. We propose a new deep learning framework for it, which adopts neural networks to better learn both user and item representations and make these close to binary codes such that the quantization loss is minimized. In addition, we extend the proposed framework for out-of-sample cases, i.e., dealing with new users, new items, and new ratings. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed framework.

Original languageEnglish (US)
Pages (from-to)64-75
Number of pages12
JournalKnowledge-Based Systems
StatePublished - May 15 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Management Information Systems
  • Information Systems and Management
  • Artificial Intelligence


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