Self-learning Recommendation System Using Reinforcement Learning

Purpose: This work aims to develop and evaluate a recommendation system using reinforcement learning methodology. This methodology uses RFM (Recency, Frequency, Monetary value) analysis to leverage customer segmentation. It also incorporates contextual aspects of purchasing, such as the day and part of the month, to enhance the accuracy of product recommendations by aligning offers with individual customer preferences and needs. Design/Methodology/Approach: The study involves implementing and simulating customer behavior to initiate a self-learning process, which is crucial for adapting and optimizing recommendations in dynamic markets where direct customer feedback is limited. The methodology includes detailed customer segmentation using data on the last purchase, purchase frequency, and total expenditure from [specific data sources] to identify groups with different purchasing profiles. These profiles are integrated with contextual shopping data to define states representing distinct shopping scenarios. A self-learning process, facilitated by simulations, iteratively optimizes the value function to improve the system's ability to anticipate and meet customer needs. Findings: The research shows that advanced customer segmentation combined with contextual analysis and reinforcement learning significantly improves recommendation system performance. The iterative optimization, which involves [specific process], increases the system's accuracy in anticipating and fulfilling customer needs. Practical Implications: This approach significantly enhances the shopping experience and customer satisfaction by delivering more personalized recommendations. It also provides valuable insights for optimizing marketing and sales strategies across various e-commerce industries, thereby keeping the audience informed about the real-world applications of the research. Originality/Value: This study offers a novel combination of customer segmentation, contextual analysis, and reinforcement learning. It demonstrates that this integrated approach can significantly improve recommendation system efficiency, thereby making a valuable contribution to the field of marketing strategies and customer-focused recommendations.