A Survey of Recommender System Techniques and the Ecommerce Domain

Authors: Imran Hossain, Md Aminul Haque Palash, Anika Tabassum Sejuty, Noor A Tanjim, MD Abdullah AL Nasim, Sarwar Saif, Abu Bokor Suraj, Md Mahim Anjum Haque, Nazmul Karim

22 pages, 13 figures
License: CC ZERO 1.0

Abstract: In this big data era, it is hard for the current generation to find the right data from the huge amount of data contained within online platforms. In such a situation, there is a need for an information filtering system that might help them find the information they are looking for. In recent years, a research field has emerged known as recommender systems. Recommenders have become important as they have many real-life applications. This paper reviews the different techniques and developments of recommender systems in e-commerce, e-tourism, e-resources, e-government, e-learning, and e-library. By analyzing recent work on this topic, we will be able to provide a detailed overview of current developments and identify existing difficulties in recommendation systems. The final results give practitioners and researchers the necessary guidance and insights into the recommendation system and its application.

Submitted to arXiv on 15 Aug. 2022

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Beta

Look for similar papers (in beta version)

By clicking on the button above, our algorithm compares the title and abstract of this paper with all the papers of the same arXiv category that we have summarized, and finds the closest ones. You can rerun it from time to time: the results improve as our database grows.