Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2023

Abstract

Variational Autoencoder (VAE) offers a non-linear probabilistic modeling of user's preferences. While it has achieved remarkable performance at collaborative filtering, it typically samples a single vector for representing user's preferences, which may be insufficient to capture the user's diverse interests. Existing solutions extend VAE to model multiple interests of users by resorting a variant of self-attentive method, i.e., employing prototypes to group items into clusters, each capturing one topic of user's interests. Despite showing improvements, the current design could be more effective since prototypes are randomly initialized and shared across users, resulting in uninformative and non-personalized clusters.To fill the gap, firstly, we introduce iterative latent attention for personalized item grouping into VAE framework to infer multiple interests of users. Secondly, we propose to incorporate implicit differentiation to improve training of our iterative refinement model. Thirdly, we study the self-attention to refine cluster prototypes for item grouping, which is largely ignored by existing works. Extensive experiments on three real-world datasets demonstrate stronger performance of our method over those of baselines.librar

Keywords

Information systems, Information retrieval, Recommender systems, Variational Autoencoder

Discipline

Applied Statistics | Artificial Intelligence and Robotics | Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Birmingham, United Kingdom, 2023, October 21-25

First Page

2462

Last Page

2471

Identifier

10.1145/3583780.3614980

Publisher

Association for Computing Machinery

City or Country

New York, NY, United States

Additional URL

https://doi.org/10.1145/3583780.3614980

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