Hybrid Recommender System for E-Commerce: A Comprehensive Review and Future Directions

Abstract

Recommender systems are widely used in e-commerce platforms to provide personalized recommendations to users, thereby enhancing user experience and increasing sales. Traditional recommender systems, such as content-based and collaborative filtering, have their limitations in terms of accuracy and scalability. Hybrid recommender systems, which combine multiple recommendation techniques, have emerged as a promising solution to overcome these limitations and improve recommendation performance. In this research paper, we present a comprehensive review of the state-of-the-art hybrid recommender systems for e-commerce, focusing on the different techniques and approaches used in hybrid recommendation, including content-based, collaborative filtering, and hybridization techniques. We also highlight the advantages and challenges of using hybrid recommender systems in e-commerce, including data sparsity, scalability, and interpretability. Furthermore, we discuss the evaluation metrics used for measuring the performance of hybrid recommender systems and identify the research gaps and future directions in this field. Overall, this paper provides a comprehensive overview of the current research on hybrid recommender systems for e-commerce and offers insights into the future directions for further research and development in this area.