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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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 |
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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 |
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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. |
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text |
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LIU, Zhongzhou FANG, Yuan WU, Min |
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LIU, Zhongzhou FANG, Yuan WU, Min |
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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 |
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Institutional Knowledge at Singapore Management University |
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2023 |
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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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