Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2022

Abstract

Classical recommendation methods typically render user representation as a single vector in latent space. Oftentimes, a user's interactions with items are influenced by several hidden factors. To better uncover these hidden factors, we seek disentangled representations. Existing disentanglement methods for recommendations are mainly concerned with user-item interactions alone. To further improve not only the effectiveness of recommendations but also the interpretability of the representations, we propose to learn a second set of disentangled user representations from textual content and to align the two sets of representations with one another. The purpose of this coupling is two-fold. For one benefit, we leverage textual content to resolve sparsity of user-item interactions, leading to higher recommendation accuracy. For another benefit, by regularizing factors learned from user-item interactions with factors learned from textual content, we map uninterpretable dimensions from user representation into words. An attention-based alignment is introduced to align and enrich hidden factors representations. A series of experiments conducted on four real-world datasets show the efficacy of our methods in improving recommendation quality.

Keywords

disentangled representation, textual content-aware recommender systems, user preferences interpretation

Discipline

Numerical Analysis and Scientific Computing | Software Engineering

Research Areas

Data Science and Engineering

Publication

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, August

First Page

1798

Last Page

1806

ISBN

9781450393850

Identifier

10.1145/3534678.3539474

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3534678.3539474

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