Soft Computing and Machine Intelligence

Institute of Information Science and Technology (정보과학기술연구소)
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Improving the Product Recommendation System based-on Customer Interest for Online Shopping Using Deep Reinforcement Learning

  • Published : 2021.06.01
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Abstract

In recent years, due to COVID-19, the process of shopping has become more restricted and difficult for customers. Based on this aspect, customers are more interested in online shopping to keep the Untact rules and stay safe, similarly ordering their product based on their need and interest with most straightforward and fastest ways. In this paper, the reinforcement learning technique is applied in the product recommendation system to improve the recommendation system quality for better and more related suggestions based on click patterns and users' profile information. The dataset used in this system was taken from an online shopping mall in Jeju island, South Korea. We have compared the proposed method with the recent state-of-the-art and research results, which show that reinforcement learning effectiveness is higher than other approaches.

Keywords

Acknowledgement

This research was financially supported by the Ministry of Small and Medium-sized Enterprises(SMEs) and Startups(MSS), Korea, under the "Regional Specialized Industry Development Program(RD, S2855401)" supervised by the Korea Institute for Advancement of Technology(KIAT).

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