Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2024

Abstract

We seek to uncover the latent interest units from behavioral data to better learn user preferences under the VAE framework. Existing practices tend to ignore the multiple facets of item characteristics, which may not capture it at appropriate granularity. Moreover, current studies equate the granularity of item space to that of user interests, which we postulate is not ideal as user interests would likely map to a small subset of item space. In addition, the compositionality of user interests has received inadequate attention, preventing the modeling of interactions between explanatory factors driving a user's decision. To resolve this, we propose to align user interests with multi-faceted item characteristics. First, we involve prototype-based representation learning to discover item characteristics along multiple facets. Second, we compose user interests from uncovered item characteristics via binding mechanism, separating the granularity of user preferences from that of item space. Third, we design a dedicated bi-directional binding block, aiding the derivation of compositional user interests. On real-world datasets, the experimental results demonstrate the strong performance of our proposed method compared to a series of baselines.

Keywords

multi-faceted representation, user interests, item characteristics

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 12th International Conference on Learning Representations, Vienna, Austria, ICLR 2024, May 7

First Page

1

Last Page

16

Publisher

ICLR

City or Country

Vienna, Austria

Copyright Owner and License

Authors

Additional URL

https://openreview.net/forum?id=MzjiMxlWab

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