Recommender Systems in E-commerce

TL;DR

This paper reviews the challenges faced by e-commerce recommender systems, such as cold start and scalability, and discusses potential solutions, different system types, and future research directions to enhance their effectiveness.

Contribution

It provides a comprehensive overview of challenges, solutions, and system types in e-commerce recommender systems, highlighting areas for future research.

Findings

Identifies key challenges like cold start and scalability.

Discusses various types of recommender systems and their pros and cons.

Suggests future research directions for improving system performance.

Abstract

E-commerce recommender systems are becoming increasingly important in the current digital world. They are used to personalize user experience, help customers find what they need quickly and efficiently, and increase revenue for the business. However, there are several challenges associated with big data-based e-commerce recommender systems. These challenges include limited resources, data validity period, cold start, long tail problem, scalability. In this paper, we discuss the challenges and potential solutions to overcome these challenges. We also discuss the different types of e-commerce recommender systems, their advantages, and disadvantages. We conclude with some future research directions to improve the performance of e-commerce recommender systems.