Abstract
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.
Highlights
With the increasing amount of information and access to information getting more and more smooth, users’ choice towards goods, movies, and restaurants has significantly increased
Mass information brings more convenience; on the other hand, information overload brings the trouble of overchoice as well. e recommender system is the information filtering tool that deals with such problem through providing users information with guiding significance in a highly personalized manner [1]
Ere are two main DRL algorithms applied in the recommender system, including DQN and actor-critic, which are elaborated in related works
Summary
With the increasing amount of information and access to information getting more and more smooth, users’ choice towards goods, movies, and restaurants has significantly increased. E recommender system is the information filtering tool that deals with such problem through providing users information with guiding significance in a highly personalized manner [1]. As a subarea of machine learning, deep reinforcement learning-based recommender systems have gained significant attention by overcoming drawbacks of traditional methods and achieving high recommendation quality. Considering the Q value-based deep reinforcement learning algorithm is only suitable for low-dimensional and discrete motion spaces, as it is well known that DQN was first proposed in Atari games, which only have four actions. The actor-critic-based deep reinforcement learning algorithm is not limited to discrete motion space and even can handle continuous motion space, so the algorithm in this study is under the framework of actor-critic. We apply DDPG as our basic algorithm
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