Learning binary codes with neural collaborative filtering for efficient recommendation systems

doi:10.1016/j.knosys.2019.02.012

Learning binary codes with neural collaborative filtering for efficient recommendation systems

2019-05-01

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DOI：10.1016/j.knosys.2019.02.012

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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. (C) 2019 Elsevier B.V. All rights reserved.

Keywords:

Recommendation systems

Binary code learning

Neural networks

Neural collaborative hashing

Learning binary codes with neural collaborative filtering for efficient recommendation systems

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