Enhancing E-commerce Hybrid Recommendation Systems Using Metaheuristic Optimization Techniques

Abstract

This study investigates the complicated nexus of recommender systems and sentiment analysis within e-commerce, with the goal of improving the customer experience and providing personalized product recommendations. Beginning with a thorough study introduction, the paper covers the background of the field, explaining the fundamental concepts of recommender systems, design principles, and the vital role of customer interest predictions. The exploration continues with the central theme of sentiment analysis in identifying user preferences with a complete explanation of sentiment analysis techniques such as rule-based, machine learning and deep learning methods. The integration of sentiment analysis into recommender systems is addressed, covering preprocessing, feature engineering, and collaborative filtering approaches. As the research goes beyond theoretical foundations, it evaluates sentiment analysis by processing customer reviews, detecting sentiments and using review metadata for sophisticated analysis.

Robust evaluation methodologies, metrics, and cross-validation techniques are explained, considering the multidimensionality of sentiment analysis. The chapter also links to domain-specific evaluation issues, user-centered evaluations, and ethical dimensions, thus, providing the challenges and ethical aspects of user feedback and interaction analysis. The toolbox of sentiment analysis that combines various NLP libraries, machine learning frameworks, sentiment analysis APIs, and custom models is opened, providing a complete picture of the available tools for practitioners. The study brought to light the real-world applications of sentiment-sensitive recommendation systems in personalized product recommendations, dynamic pricing, customer feedback analysis, social media marketing campaigns, user experience optimization, and brand reputation management. The implementation of the research offers a pathway for future trends such as integration with conversational AI, advancements in multimodal sentiment analysis, and ethical considerations. The literature review provides a strong basis for the research by exploring theories such as the Theory of Reasoned Action and Human Society Theory. The methodology section details how the study was conducted, including the participants, data collection, and analysis methods. The results and discussions present the findings and their significance. Finally, the research concludes with a summary, highlighting the implications of the findings for the development of sentiment-aware recommendation systems. It emphasizes the need for ongoing research, ethical considerations, and user education in this rapidly evolving field.
Keywords: Machine Learning, E-commerce, Sentiment Analysis Techniques, Personalized Product Recommendations