Reinforcement Learning Recommendation Algorithm Based on Label Value Distribution

Zhida Guo, Jingyuan Fu () and Peng Sun
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Zhida Guo: School of Economics and Management, Dalian Jiaotong University, Dalian 116021, China
Jingyuan Fu: Faculty of Education, The University of Hong Kong, Hong Kong 999077, China
Peng Sun: Institute of Computing Technology, China Academy of Railway Sciences, Beijing 100081, China

Mathematics, 2023, vol. 11, issue 13, 1-15

Abstract: Reinforcement learning is an important machine learning method and has become a hot popular research direction topic at present in recent years. The combination of reinforcement learning and a recommendation system, is a very important application scenario and application, and has always received close attention from researchers in all sectors of society. In this paper, we first propose a feature engineering method based on label distribution learning, which analyzes historical behavior is analyzed and constructs, whereby feature vectors are constructed for users and products via label distribution learning. Then, a recommendation algorithm based on value distribution reinforcement learning is proposed. We first designed the stochastic process of the recommendation process, described the user’s state in the interaction process (by including the information on their explicit state and implicit state), and dynamically generated product recommendations through user feedback. Next, by studying hybrid recommendation strategies, we combined the user’s dynamic and static information to fully utilize their information and achieve high-quality recommendation algorithms. Finally, the algorithm was designed and validated, and various relevant baseline models were compared to demonstrate the effectiveness of the algorithm in this study. With this study, we actually tested the remarkable advantages of relevant design models based on nonlinear expectations compared to other homogeneous individual models. The use of recommendation systems with nonlinear expectations has considerably increased the accuracy, data utilization, robustness, model convergence speed, and stability of the systems. In this study, we incorporated the idea of nonlinear expectations into the design and implementation process of recommendation systems. The main practical value of the improved recommendation model is that its performance is more accurate than that of other recommendation models at the same level of computing power level. Moreover, due to the higher amount of information that the enhanced model contains, it provides theoretical support and the basis for an algorithm that can be used to achieve high-quality recommendation services, and it has many application prospects.

Keywords: recommendation system; neural networks; value distribution reinforcement learning; label distribution learning; nonlinear expectation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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