  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.
