Research on interactive recommendation system based on reinforcement learning

Qifan Wang; Hongwei Shi

doi:10.1117/12.2624858

13 December 2021 Research on interactive recommendation system based on reinforcement learning

Qifan Wang, Hongwei Shi

Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 120870X (2021) https://doi.org/10.1117/12.2624858
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China

Abstract

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.

Citation Download Citation

Qifan Wang and Hongwei Shi "Research on interactive recommendation system based on reinforcement learning", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 120870X (13 December 2021); https://doi.org/10.1117/12.2624858

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KEYWORDS

Data modeling

Performance modeling

Data processing

Neural networks

Systems modeling

Vector spaces

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