E-Commerce Recommender Systems: A Comprehensive Survey

Recommender systems, systems designed to predict a user's preferences or evaluations for a product, have become integral to e-commerce applications in recent years. These systems are prevalent on popular e-commerce websites that sell products, books, and music. Beyond e-commerce, recommender systems are also utilized for various other sectors such as restaurants, humor, life insurance, financial services, and even Twitter followers. The development of the web and recommender systems has been a simultaneous process, with each influencing the other. To enhance the quality of the recommendation process, these systems are now incorporating social information. Recommended systems use local, implicit, and personal data collected from the Internet to refine their processes for future improvements. This study aims to review and summarize the latest developments in the techniques used by e-commerce recommender systems. Moreover, the review of the recent literature has identified key findings, significant research gaps, and notable drawbacks in the development and application of these systems. The objective is to provide a comprehensive picture of the current state of the field and indicate potential areas for future research, to contribute to the growth and sophistication of recommender systems.