A Survey on Variational Autoencoders in Recommender Systems
Article No.: 268, Pages 1 - 40
Abstract
Recommender systems have become an important instrument to connect people to information. Sparse, complex, and rapidly growing data presents new challenges to traditional recommendation algorithms. To overcome these challenges, various deep learning-based recommendation algorithms have been proposed. Among these, Variational AutoEncoder (VAE)-based recommendation methods stand out. VAEs are based on a flexible probabilistic framework, which is robust for data sparsity and compatible with other deep learning-based models for dealing with multimodal data. In addition, the deep generative structure of VAEs helps to perform Bayesian inference in an efficient manner. VAE-based recommendation algorithms have given rise to many novel graphical models, and they have achieved promising performance. In this article, we conduct a survey to systematically summarize recent VAE-based recommendation algorithms. Four frequently used characteristics of VAE-based recommendation algorithms are summarized, and a taxonomy of VAE-based recommendation algorithms is proposed. We also identify future research directions for, advanced perspectives on, and the application of VAEs in recommendation algorithms, to inspire future work on VAEs for recommender systems.
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Index Terms
- A Survey on Variational Autoencoders in Recommender Systems
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October 2024
954 pages
ISSN:0360-0300
EISSN:1557-7341
DOI:10.1145/3613652
- Editors:
- David Atienza,
- Michela Milano
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Publication History
Published: 24 June 2024
Online AM: 15 May 2024
Accepted: 22 April 2024
Revised: 09 April 2024
Received: 30 June 2022
Published in CSUR Volume 56, Issue 10
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- National Natural Science Foundation of China
- Research Foundation of Science and Technology Plan Project of Guangzhou City
- Zhuhai City Industry-University-Research Project
- Technology Innovation Center for Collaborative Applications of Natural Resources Data in GBA, MNR
- Pazhou Lab
- Dutch Ministry of Education, Culture and Science
- Netherlands Organisation for Scientific Research
- NWA
- Dutch Research Council (NWO)
- Dutch Research Council (NWO), DPG Media, RTL, and the Dutch Ministry of Economic Affairs and Climate Policy (EZK)
- European Union’s Horizon Europe research and innovation
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