Enhancing AI-Driven Product Discovery Tools Using Reinforcement Learning and Collaborative Filtering Algorithms

Abstract

This research paper explores the integration of reinforcement learning (RL) with collaborative filtering (CF) algorithms to enhance AI-driven product discovery tools. The emergence of e-commerce has dramatically increased the need for efficient product discovery mechanisms that can deliver personalized and relevant recommendations to users. Traditional recommendation systems have relied on collaborative filtering to harness user-item interaction data, but these methods often struggle with scalability and dynamic user preferences. In response, this study proposes a novel framework that fuses reinforcement learning with collaborative filtering to address these limitations. The reinforcement learning model, designed to adaptively learn user preferences and optimize long-term user engagement, is integrated with collaborative filtering to leverage rich historical interaction data. Our experiments demonstrate that the proposed RL-CF hybrid system exceeds the performance of standalone collaborative filtering models in terms of precision, recall, and user satisfaction across multiple datasets. Furthermore, an in-depth analysis reveals the framework’s effectiveness in mitigating the cold-start problem and improving recommendation diversity. The results emphasize the potential of reinforcement learning to not only refine existing CF methods but also to pioneer new avenues in product discovery applications. This research paves the way for developing more robust, scalable, and user-centric AI-driven recommendation systems in the e-commerce domain.