Version 1
: Received: 8 January 2021 / Approved: 11 January 2021 / Online: 11 January 2021 (10:02:49 CET)
How to cite:
Zhao, Z.; Cao, L.; Chen, X.; Xu, Z. Tag-Aware Recommender System Based on Deep Reinforcement Learning. Preprints2021, 2021010176. https://doi.org/10.20944/preprints202101.0176.v1
Zhao, Z.; Cao, L.; Chen, X.; Xu, Z. Tag-Aware Recommender System Based on Deep Reinforcement Learning. Preprints 2021, 2021010176. https://doi.org/10.20944/preprints202101.0176.v1
Zhao, Z.; Cao, L.; Chen, X.; Xu, Z. Tag-Aware Recommender System Based on Deep Reinforcement Learning. Preprints2021, 2021010176. https://doi.org/10.20944/preprints202101.0176.v1
APA Style
Zhao, Z., Cao, L., Chen, X., & Xu, Z. (2021). Tag-Aware Recommender System Based on Deep Reinforcement Learning. Preprints. https://doi.org/10.20944/preprints202101.0176.v1
Chicago/Turabian Style
Zhao, Z., Xiliang Chen and Zhixiong Xu. 2021 "Tag-Aware Recommender System Based on Deep Reinforcement Learning" Preprints. https://doi.org/10.20944/preprints202101.0176.v1
Abstract
Recently, the application of deep reinforcement learning in recommender system is flourishing and stands out by overcoming drawbacks of traditional methods and achieving high recommendation quality. The dynamics, long-term returns and sparse data issues in recommender system have been effectively solved. But the application of deep reinforcement learning brings problems of interpretability, overfitting, complex reward function design, and user cold start. This paper proposed a tag-aware recommender system based on deep reinforcement learning without complex function design, taking advantage of tags to make up for the interpretability problems existing in recommender system. Our experiment is carried out on MovieLens dataset. The result shows that, DRL based recommender system is superior than traditional algorithms in minimum error and the application of tags has little effect on accuracy when making up for interpretability. In addition, DRL based recommender system has excellent performance on user cold start problems.
Keywords
recommender system; tag-ware; deep reinforcement learning; user cold start
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
