Enhancing Personalized Advertising with Reinforcement Learning and Natural Language Processing Techniques

Abstract

This research paper explores the integration of reinforcement learning (RL) and natural language processing (NLP) to advance personalized advertising strategies. With the exponential growth of digital content and consumer interaction data, advertisers face the challenge of delivering relevant and personalized experiences to users. We propose a novel framework that leverages RL algorithms to dynamically adapt advertising strategies by learning from user interactions and feedback. Concurrently, NLP techniques are employed to analyze and understand textual data, enabling the extraction of user preferences and sentiment from unstructured data sources. Our approach involves a comprehensive methodology, where user profiles are continuously updated through real-time interaction data, allowing the RL model to optimize ad targeting and placement decisions. The integration of NLP facilitates the interpretation of user-generated content, providing deeper insights into consumer behavior and enhancing the accuracy of personalization. Experimental results demonstrate significant improvements in key performance indicators, such as click-through rates and conversion rates, compared to traditional advertising methods. Through case studies, we highlight the system's ability to adapt to evolving user preferences and contextual changes, underscoring its potential to revolutionize personalized advertising. This study contributes to the field by presenting an efficient, scalable solution that maximizes advertising efficiency through AI-driven personalization, setting a new benchmark for future research and application in the industry.