Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2023

Abstract

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.

Keywords

Adaptation models, Adaptive models, Data models, dual-view user preferences, Electronic commerce, Motion pictures, Noise measurement, personalized recommendation systems, Recommender systems, Semantics

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Knowledge and Data Engineering

First Page

1

Last Page

12

ISSN

1041-4347

Identifier

10.1109/TKDE.2023.3236370

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TKDE.2023.3236370

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