Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2025

Abstract

Frameworks for discovering multiple user interest factors based on Variational AutoEncoder (VAE) has demonstrated competitive recommendation performance. However, as VAE only considers one user as input at a time, sharing across like-minded users may not be adequately facilitated. Moreover, interest sharing between users is not always available and thus, poses a challenge for VAE to explicitly model this information. To resolve this, we introduce an inter-user memory-based mechanism to unsupervisedly discover latent interest sharing between users under VAE framework. Concretely, we design a memory including an array of prototypes, each hypothetically representing a group of users sharing a particular interest. These memory prototypes are jointly trained with the backbone VAE-based recommendation model. For each user, we first discover multiple intra-user interest factors behind their item adoptions. Next, intra-user interest factors query to memory to retrieve the inter-user interest clues from like-minded users. This query-retrieve process is performed sequentially via a series of attention-transformation steps. Then, interest clues retrieved from memory are incorporated into interest factor representations of each user to increase their expressiveness. Thorough experiments on real-world datasets verify the strength of our method over an array of baselines. We further conduct qualitative analysis to understand the inner working of our memory-based refinement approach.

Keywords

Multi-interest representation, inter-user memory, VAE

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

WSDM '25: Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, Hannover, Germany, 2025 March 10-14

First Page

156

Last Page

164

ISBN

9798400713293

Identifier

10.1145/3701551.3703558

Publisher

ACM

City or Country

New York

Embargo Period

7-14-2025

Copyright Owner and License

Authors

Additional URL

http://doi.org/10.1145/3701551.3703558

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