Leveraging Reinforcement Learning and Neural Collaborative Filtering for Enhanced AI-Driven Personalized Marketing Campaigns

Abstract

This research paper explores the synergistic integration of reinforcement learning (RL) and neural collaborative filtering (NCF) to enhance personalized marketing campaigns driven by artificial intelligence (AI). In recent years, the marketing industry has increasingly adopted AI technologies to deliver customized experiences to consumers. However, achieving real-time and contextually relevant personalization remains a complex challenge. Our study presents a novel framework that utilizes RL to dynamically adapt to consumer behavior and preferences while employing NCF to predict user-item interactions with high accuracy. We evaluate the proposed framework using a comprehensive dataset from a major e-commerce platform, demonstrating its ability to significantly enhance user engagement and conversion rates compared to traditional methods. Specifically, the RL component optimizes decision-making policies by learning from continuous interactions with users, thereby enabling the system to refine marketing strategies in response to real-time feedback. Concurrently, the NCF module leverages deep learning architectures to uncover latent factors in user-item matrices, facilitating precise recommendation generation. The integration of these methodologies not only improves the temporal relevance of marketing campaigns but also increases their adaptability across diverse consumer segments. Our findings highlight the potential of combining RL and NCF in creating AI-driven marketing solutions that are both effective and scalable, paving the way for more refined and impactful personalized marketing strategies.