Learning binary codes with neural collaborative filtering for efficient recommendation systems

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...

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Main Authors:	Li, Yang, Wang, Suhang, Pan, Quan, Peng, Haiyun, Yang, Tao, Cambria, Erik
Other Authors:	School of Computer Science and Engineering
Format:	Article
Language:	English
Published:	2021
Subjects:	Engineering::Computer science and engineering Recommendation Systems Binary Code Learning
Online Access:	https://hdl.handle.net/10356/151678
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-151678
record_format	dspace
spelling	sg-ntu-dr.10356-1516782021-07-14T07:30:39Z Learning binary codes with neural collaborative filtering for efficient recommendation systems Li, Yang Wang, Suhang Pan, Quan Peng, Haiyun Yang, Tao Cambria, Erik School of Computer Science and Engineering Engineering::Computer science and engineering Recommendation Systems Binary Code Learning 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. 2021-07-14T07:30:39Z 2021-07-14T07:30:39Z 2019 Journal Article 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 0950-7051 https://hdl.handle.net/10356/151678 10.1016/j.knosys.2019.02.012 2-s2.0-85061598416 172 64 75 en Knowledge-Based Systems © 2019 Elsevier B.V. All rights reserved.
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Computer science and engineering Recommendation Systems Binary Code Learning
spellingShingle	Engineering::Computer science and engineering Recommendation Systems Binary Code Learning Li, Yang Wang, Suhang Pan, Quan Peng, Haiyun Yang, Tao Cambria, Erik Learning binary codes with neural collaborative filtering for efficient recommendation systems
description	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.
author2	School of Computer Science and Engineering
author_facet	School of Computer Science and Engineering Li, Yang Wang, Suhang Pan, Quan Peng, Haiyun Yang, Tao Cambria, Erik
format	Article
author	Li, Yang Wang, Suhang Pan, Quan Peng, Haiyun Yang, Tao Cambria, Erik
author_sort	Li, Yang
title	Learning binary codes with neural collaborative filtering for efficient recommendation systems
title_short	Learning binary codes with neural collaborative filtering for efficient recommendation systems
title_full	Learning binary codes with neural collaborative filtering for efficient recommendation systems
title_fullStr	Learning binary codes with neural collaborative filtering for efficient recommendation systems
title_full_unstemmed	Learning binary codes with neural collaborative filtering for efficient recommendation systems
title_sort	learning binary codes with neural collaborative filtering for efficient recommendation systems
publishDate	2021
url	https://hdl.handle.net/10356/151678
_version_	1707050410641981440

Learning binary codes with neural collaborative filtering for efficient recommendation systems

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