Dual-view preference learning for adaptive recommendation

While recommendation systems have been widely deployed, most existing approaches only capture user preferences in the , i.e., the user's general interest across all kinds of items. However, in real-world scenarios, user preferences could vary with items of different natures, which we call the ....

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Main Authors: LIU, Zhongzhou, FANG, Yuan, WU, Min
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Language:English
Published: Institutional Knowledge at Singapore Management University 2023
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Online Access:https://ink.library.smu.edu.sg/sis_research/7766
https://ink.library.smu.edu.sg/context/sis_research/article/8769/viewcontent/Dual_View_av.pdf
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spelling sg-smu-ink.sis_research-87692023-02-23T08:07:02Z Dual-view preference learning for adaptive recommendation LIU, Zhongzhou FANG, Yuan WU, Min While recommendation systems have been widely deployed, most existing approaches only capture user preferences in the , i.e., the user's general interest across all kinds of items. However, in real-world scenarios, user preferences could vary with items of different natures, which we call the . Both views are crucial for fully personalized recommendation, where an underpinning macro-view governs a multitude of finer-grained preferences in the micro-view. To model the dual views, in this paper, we propose a novel model called Dual-View Adaptive Recommendation (DVAR). In DVAR, we formulate the micro-view based on item categories, and further integrate it with the macro-view. Moreover, DVAR is designed to be adaptive, which is capable of automatically adapting to the dual-view preferences in response to different input users and item categories. To the best of our knowledge, this is the first attempt to integrate user preferences in macro- and micro- views in an adaptive way, without relying on additional side information such as text reviews. Finally, we conducted extensive quantitative and qualitative evaluations on several real-world datasets. Empirical results not only show that DVAR can significantly outperform other state-of-the-art recommendation systems, but also demonstrate the benefit and interpretability of the dual views. 2023-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7766 info:doi/10.1109/TKDE.2023.3236370 https://ink.library.smu.edu.sg/context/sis_research/article/8769/viewcontent/Dual_View_av.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 Adaptation models Adaptive models Data models dual-view user preferences Electronic commerce Motion pictures Noise measurement personalized recommendation systems Recommender systems Semantics Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Adaptation models
Adaptive models
Data models
dual-view user preferences
Electronic commerce
Motion pictures
Noise measurement
personalized recommendation systems
Recommender systems
Semantics
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Adaptation models
Adaptive models
Data models
dual-view user preferences
Electronic commerce
Motion pictures
Noise measurement
personalized recommendation systems
Recommender systems
Semantics
Databases and Information Systems
Numerical Analysis and Scientific Computing
LIU, Zhongzhou
FANG, Yuan
WU, Min
Dual-view preference learning for adaptive recommendation
description While recommendation systems have been widely deployed, most existing approaches only capture user preferences in the , i.e., the user's general interest across all kinds of items. However, in real-world scenarios, user preferences could vary with items of different natures, which we call the . Both views are crucial for fully personalized recommendation, where an underpinning macro-view governs a multitude of finer-grained preferences in the micro-view. To model the dual views, in this paper, we propose a novel model called Dual-View Adaptive Recommendation (DVAR). In DVAR, we formulate the micro-view based on item categories, and further integrate it with the macro-view. Moreover, DVAR is designed to be adaptive, which is capable of automatically adapting to the dual-view preferences in response to different input users and item categories. To the best of our knowledge, this is the first attempt to integrate user preferences in macro- and micro- views in an adaptive way, without relying on additional side information such as text reviews. Finally, we conducted extensive quantitative and qualitative evaluations on several real-world datasets. Empirical results not only show that DVAR can significantly outperform other state-of-the-art recommendation systems, but also demonstrate the benefit and interpretability of the dual views.
format text
author LIU, Zhongzhou
FANG, Yuan
WU, Min
author_facet LIU, Zhongzhou
FANG, Yuan
WU, Min
author_sort LIU, Zhongzhou
title Dual-view preference learning for adaptive recommendation
title_short Dual-view preference learning for adaptive recommendation
title_full Dual-view preference learning for adaptive recommendation
title_fullStr Dual-view preference learning for adaptive recommendation
title_full_unstemmed Dual-view preference learning for adaptive recommendation
title_sort dual-view preference learning for adaptive recommendation
publisher Institutional Knowledge at Singapore Management University
publishDate 2023
url https://ink.library.smu.edu.sg/sis_research/7766
https://ink.library.smu.edu.sg/context/sis_research/article/8769/viewcontent/Dual_View_av.pdf
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