Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151678
Title: Learning binary codes with neural collaborative filtering for efficient recommendation systems
Authors: Li, Yang
Wang, Suhang
Pan, Quan
Peng, Haiyun
Yang, Tao
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Li, Y., Wang, S., Pan, Q., Peng, H., Yang, T. & Cambria, E. (2019). Learning binary codes with neural collaborative filtering for efficient recommendation systems. Knowledge-Based Systems, 172, 64-75. https://dx.doi.org/10.1016/j.knosys.2019.02.012
Journal: Knowledge-Based Systems
Abstract: 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.
URI: https://hdl.handle.net/10356/151678
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2019.02.012
Rights: © 2019 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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