Vol. 1, Issue 1, Part A (2024)
Integrating data mining and reinforcement learning for real-time recommendation systems
Sabbir Alam and Faisal Hasan
With the rapid digitization of services across sectors, the demand for real-time, intelligent, and adaptive recommendation systems has intensified. While traditional recommendation algorithms based on collaborative filtering and content-based approaches have achieved substantial success, they often fall short in dynamic environments where user preferences evolve continuously. This paper presents an integrated approach that combines data mining techniques with reinforcement learning (RL) to develop robust, real-time recommendation systems. Data mining facilitates the discovery of patterns and latent features from historical data, serving as foundational knowledge, whereas reinforcement learning adapts to user behavior dynamically through real-time interaction and feedback. This synergy allows the system to achieve personalization at scale, maintain contextual awareness, and respond to non-stationary user interests. The paper further presents architecture, practical applications, experimental results, and challenges, culminating in a forward-looking discussion on future enhancements, including deep reinforcement learning and privacy-aware systems.
Pages: 24-27 | 14 Views 5 Downloads