Dual-View Preference Learning for Adaptive Recommendation

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Dual-View Preference Learning for Adaptive Recommendation
Title:
Dual-View Preference Learning for Adaptive Recommendation
Journal Title:
IEEE Transactions on Knowledge and Data Engineering
Publication Date:
12 January 2023
Citation:
Liu, Z., Fang, Y., & Wu, M. (2023). Dual-View Preference Learning for Adaptive Recommendation. IEEE Transactions on Knowledge and Data Engineering, 1–12. https://doi.org/10.1109/tkde.2023.3236370
Abstract:
While recommendation systems have been widely deployed, most existing approaches only capture user preferences in the macro-view, 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 micro-view. 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.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Ministry of Education - Academic Research Fund Tier 2
Grant Reference no. : MOE-T2EP20122-0041
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
1558-2191
2326-3865
1041-4347
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