A Comprehensive Analysis of Recommendation Algorithms Based on Deep Reinforcement Learning

DOI

10.2991/978-94-6463-300-9_36How to use a DOI?

Keywords

Deep reinforcement learning; recommender system; policy optimization; model prediction

Abstract

Contemporary recommendation systems encounter the challenges posed by information overload and personalized user needs. Recently, there has been a widespread application of deep reinforcement learning algorithms (DRL) to tackle the aforementioned issues. The paper provides a detailed introduction to the basic principles and associated algorithms of DRL. It categorizes recommendation algorithms employing deep reinforcement learning into single-agent RL and multi-agent RL. Representative directions in each category are introduced, their design concepts analyzed, and the advantages and disadvantages of these methods summarized. Specifically, an in-depth analysis of single-agent algorithms is performed. These algorithms are categorized into model-free RL, model-based RL, and hierarchical RL, and the characteristics and current status of each method are discussed. Finally, the paper concludes by summarizing the entire content and analyzing the future research directions and corresponding development trends in this field.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rui Wang
PY  - 2023
DA  - 2023/11/27
TI  - A Comprehensive Analysis of Recommendation Algorithms Based on Deep Reinforcement Learning
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 347
EP  - 360
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_36
DO  - 10.2991/978-94-6463-300-9_36
ID  - Wang2023
ER  -