Personalize E-Commerce Product Recommendations Based on User Behavior Using Reinforced Learning Algorithms

The development of a personalized and adaptive e-commerce product recommendation system will be developed using the Reinforcement Learning algorithm in this study. Initial data is extremely promising in its ability to raise sales conversion: 30% of the products added to the cart are never purchased. Additionally, there is a strong correlation of 0.8 between viewed versus purchased products. Data collection was from 447 Indonesian respondents over a period of June to July 2024. It was collected using an online questionnaire that measures recommendation quality, satisfaction, and ease of use with purposive sampling. Partial Least Squares Structural Equation Modeling was done on the data analysis. From that, it has been found that system quality is positively related to the accuracy, novelty, and diversity of the recommendation. The results further show how this would lead to an improved user experience, satisfaction, and sales conversion with the reinforcement learning-based system. These findings give insight into developing efficient adaptive recommendation systems on e-commerce platforms and open opportunities for further research.