Research on interactive recommendation system based on reinforcement learning
Qifan Wang, Hongwei Shi · 2021
The recommendation system plays a strong auxiliary role in the user's handling of information overload. For products, user comments and descriptions are important reference data. Based on reinforcement learning, more accurate recommendations can be made through the interaction between users and product-related data items. However, the interactive recommendation will face some problems caused by data sparseness. For text data information, users and items can be mapped to the feature space to alleviate this problem. This paper proposed the depth deterministic strategy gradient algorithm is used to train the recommendation model. The experimental results on three real data sets show that the model has a relatively accurate recommendation effect and acceptable running time, and it can alleviate the impact of data sparsity on the recommendation effect to a certain extent.