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Dual-View Preference Learning for Adaptive Recommendation
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DOI：10.1109/TKDE.2023.3236370.png)
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摘要
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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.
Keyword:
Adaptation models
Recommender systems
Data models
Motion pictures
Electronic commerce
Semantics
Noise measurement
Adaptive models
dual-view user preferences
personalized recommendation systems
期刊
IF:
10.4
论文数:
6.9K
被引数:
3.2W
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