Dual-view preference learning for adaptive recommendation

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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Bibliographic Details
Main Authors: LIU, Zhongzhou, FANG, Yuan, WU, Min
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2023
Subjects:
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
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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Institution: Singapore Management University
Language: English
Description
Summary: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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