Personalized Product Recommendation Systems in E-commerce - A Hybrid Approach Using Reinforcement Learning and Natural Language Processing

Abstract

Personalized recommendation systems are central to modern e-commerce, driving customer engagement, conversion, and lifetime value. Traditional collaborative filtering and supervised deep learning methods excel at modeling historical preferences but struggle with sequential decision-making, long-term user objectives, and incorporating unstructured textual signals from reviews and queries. This paper proposes a hybrid architecture combining reinforcement learning (RL) for sequential, long-horizon personalization and advanced natural language processing (NLP) for rich representation of items and user intents. We present: (1) a unified problem formulation that models recommendation as a Markov decision process with language-enhanced state representations; (2) a modular hybrid architecture combining a transformer-based encoder for text and context, a value-based RL policy for slate recommendation, and a policy-improvement module guided by counterfactual learning; (3) mathematical derivations for objective functions, off-policy correction, and gradient estimators; (4) an evaluation framework addressing online and offline evaluation, bias and variance of estimators, and clinical business KPIs; and (5) an implementation roadmap for production deployment in cloud environments with privacy-preserving and latency-aware design choices. Extensive discussion synthesizes recent literature from deep recommendation, RL for recommender systems, and NLP for retrieval and ranking. We provide reproducible experimental blueprints, dataset recommendations, and metrics that align engineering objectives with business outcomes. The hybrid strategy balances immediate utility with learning for long-term customer satisfaction, and addresses common production concerns including scalability, safety, and interpretability.