Tag-Aware Recommender System Based on Deep Reinforcement Learning

Recently, the application of deep reinforcement learning in the 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 the recommender system have been effec...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021, Vol.2021, p.1-12
Main Authors: Zhao, Zhiruo, Chen, Xiliang, Xu, Zhixiong, Cao, Lei
Format: Article
Language:English
Subjects:
Access to information
Actors
Algorithms
Cold
Cold starts
Collaboration
Deep learning
Machine learning
Neural networks
Ratings & rankings
Recommender systems
Semantics
Tags
User behavior
User generated content
Viscosity
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Summary:Recently, the application of deep reinforcement learning in the 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 the 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 study 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 the recommender system. Our experiment is carried out on the MovieLens dataset. The result shows that the DRL-based recommender system is superior than traditional algorithms in minimum error, and the application of tags have little effect on accuracy when making up for interpretability. In addition, the DRL-based recommender system has excellent performance on user cold start problems.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/5564234