Improving the Product Recommendation System based-on Customer Interest for Online Shopping Using Deep Reinforcement Learning

  • Published : 2021.06.01


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.



  1. Lee, H.I.; Choi, I.Y.; Moon, H.S.; Kim, J.K. A Multi-Period Product Recommender System in Online Food Market based on Recurrent Neural Networks. Sustainability 2020, 12, 969.
  2. Shahbazi, Z.; Byun, Y.C. Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners. Symmetry 2020, 12, 1798.
  3. Logesh, R.; Subramaniyaswamy, V.; Malathi, D.; Sivaramakrishnan, N.; Vijayakumar, V. Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Computing and Applications 2020, 32, 2141-2164.
  4. Zhang, Z.P.; Kudo, Y.; Murai, T.; Ren, Y.G. Enhancing recommendation accuracy of item-based collaborative filtering via item-variance weighting. Applied Sciences 2019, 9, 1928.
  5. Shahbazi, Z.; Hazra, D.; Park, S.; Byun, Y.C. Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches. Symmetry 2020, 12, 1566.
  6. Zhang, Z.; Kudo, Y.; Murai, T. Neighbor selection for user-based collaborative filtering using covering-based rough sets. Annals of Operations Research 2017, 256, 359-374.
  7. De Meo, P.; Fotia, L.; Messina, F.; Rosaci, D.; Sarne, G.M. Providing recommendations in social networks by integrating local and global reputation. Information Systems 2018, 78, 58-67.
  8. Shahbazi, Z.; Jamil, F.; Byun, Y. Topic modeling in short-text using non-negative matrix factorization based on deep reinforcement learning. Journal of Intelligent & Fuzzy Systems, pp. 1-18.
  9. Bhatta, R.; Ezeife, C.; Butt, M.N. Mining Sequential Patterns of Historical Purchases for E-commerce Recommendation. International Conference on Big Data Analytics and Knowledge Discovery. Springer, 2019, pp. 57-72.
  10. Yu, W.; Zhang, H.; He, X.; Chen, X.; Xiong, L.; Qin, Z. Aesthetic-based clothing recommendation. Proceedings of the 2018 World Wide Web Conference, 2018, pp. 649-658.
  11. Shahbazi, Z.; Byun, Y.C. Product Recommendation Based on Content-based Filtering Using XGBoost Classifier. Int. J. Adv. Sci. Technol 2019, 29, 6979-6988.