Discrete social recommendation

Social recommendation, which aims at improving the performance of traditional recommender systems by considering social information, has attracted broad range of interests. As one of the most widely used methods, matrix factorization typically uses continuous vectors to represent user/item latent fe...

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Main Authors: LIU, Chenghao, WANG, Xin, LU, Tao, ZHU, Wenwu, SUN, Jianling, HOI, Steven C. H.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2019
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Online Access:https://ink.library.smu.edu.sg/sis_research/5113
https://ink.library.smu.edu.sg/context/sis_research/article/6116/viewcontent/3787_Article_Text_6845_1_10_20190701_pv_oa.pdf
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Institution: Singapore Management University
Language: English
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spelling sg-smu-ink.sis_research-61162020-04-16T07:05:30Z Discrete social recommendation LIU, Chenghao WANG, Xin LU, Tao ZHU, Wenwu SUN, Jianling HOI, Steven C. H. Social recommendation, which aims at improving the performance of traditional recommender systems by considering social information, has attracted broad range of interests. As one of the most widely used methods, matrix factorization typically uses continuous vectors to represent user/item latent features. However, the large volume of user/item latent features results in expensive storage and computation cost, particularly on terminal user devices where the computation resource to operate model is very limited. Thus when taking extra social information into account, precisely extracting K most relevant items for a given user from massive candidates tends to consume even more time and memory, which imposes formidable challenges for efficient and accurate recommendations. A promising way is to simply binarize the latent features (obtained in the training phase) and then compute the relevance score through Hamming distance. However, such a two-stage hashing based learning procedure is not capable of preserving the original data geometry in the real-value space and may result in a severe quantization loss. To address these issues, this work proposes a novel discrete social recommendation (DSR) method which learns binary codes in a unified framework for users and items, considering social information. We further put the balanced and uncorrelated constraints on the objective to ensure the learned binary codes can be informative yet compact, and finally develop an efficient optimization algorithm to estimate the model parameters. Extensive experiments on three real-world datasets demonstrate that DSR runs nearly 5 times faster and consumes only with 1/37 of its real-value competitor’s memory usage at the cost of almost no loss in accuracy. 2019-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5113 info:doi/10.1609/aaai.v33i01.3301208 https://ink.library.smu.edu.sg/context/sis_research/article/6116/viewcontent/3787_Article_Text_6845_1_10_20190701_pv_oa.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing Social Media
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Numerical Analysis and Scientific Computing
Social Media
spellingShingle Databases and Information Systems
Numerical Analysis and Scientific Computing
Social Media
LIU, Chenghao
WANG, Xin
LU, Tao
ZHU, Wenwu
SUN, Jianling
HOI, Steven C. H.
Discrete social recommendation
description Social recommendation, which aims at improving the performance of traditional recommender systems by considering social information, has attracted broad range of interests. As one of the most widely used methods, matrix factorization typically uses continuous vectors to represent user/item latent features. However, the large volume of user/item latent features results in expensive storage and computation cost, particularly on terminal user devices where the computation resource to operate model is very limited. Thus when taking extra social information into account, precisely extracting K most relevant items for a given user from massive candidates tends to consume even more time and memory, which imposes formidable challenges for efficient and accurate recommendations. A promising way is to simply binarize the latent features (obtained in the training phase) and then compute the relevance score through Hamming distance. However, such a two-stage hashing based learning procedure is not capable of preserving the original data geometry in the real-value space and may result in a severe quantization loss. To address these issues, this work proposes a novel discrete social recommendation (DSR) method which learns binary codes in a unified framework for users and items, considering social information. We further put the balanced and uncorrelated constraints on the objective to ensure the learned binary codes can be informative yet compact, and finally develop an efficient optimization algorithm to estimate the model parameters. Extensive experiments on three real-world datasets demonstrate that DSR runs nearly 5 times faster and consumes only with 1/37 of its real-value competitor’s memory usage at the cost of almost no loss in accuracy.
format text
author LIU, Chenghao
WANG, Xin
LU, Tao
ZHU, Wenwu
SUN, Jianling
HOI, Steven C. H.
author_facet LIU, Chenghao
WANG, Xin
LU, Tao
ZHU, Wenwu
SUN, Jianling
HOI, Steven C. H.
author_sort LIU, Chenghao
title Discrete social recommendation
title_short Discrete social recommendation
title_full Discrete social recommendation
title_fullStr Discrete social recommendation
title_full_unstemmed Discrete social recommendation
title_sort discrete social recommendation
publisher Institutional Knowledge at Singapore Management University
publishDate 2019
url https://ink.library.smu.edu.sg/sis_research/5113
https://ink.library.smu.edu.sg/context/sis_research/article/6116/viewcontent/3787_Article_Text_6845_1_10_20190701_pv_oa.pdf
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