Ecommerce platforms use recommendation systems to make things personal for users and increase sales. These systems are really changing the way people shop online. In fact, ecommerce sales are going to reach $6.4 trillion by 2025, which is more than 21% of all retail sales worldwide (eMarketer, Global ecommerce forecast 2024). This means we need recommendation systems that grab people’s attention and help them decide what to buy.

However, with all this growth there is not a lot of research that focuses on ecommerce recommendation systems. Most research is broader. Looks at ecommerce in general or recommendation systems in other industries. This paper wants to fill that gap by looking at 146 research articles from 2020 to 2025 that were found in the Scopus database.

The research used a tool called Bibliometrix to look at things like how many times articles were cited, who wrote them and what keywords were used. It found that 485 authors wrote these articles and used 1,179 keywords, which shows how many different fields are involved in this research. The results show that more and more research is being done in this area in 2023 and 2024 because of the increase in online shopping.

The most talked about topics are filtering, deep learning, personalization and using artificial intelligence for recommendations. The research also found that authors from countries and fields are working together which shows how international and interdisciplinary this research is. Some authors and papers are cited a lot, which means they are important in the field. They often combine computer science, information systems and marketing.

This study gives us a look at what is happening in ecommerce recommendation research and can help researchers, people who work in the field and those who build platforms make things better for users. Ecommerce recommendation systems are a part of this and this research can help us understand them better. The goal is to make things more personal, for users and improve their experience. Ecommerce platforms and recommendation systems are. This research can help us see how they work together..