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Challenges and Applications of Recommender Systems in E-Commerce

Challenges and Applications of Recommender Systems in E-Commerce

Taushif Anwar, V. Uma, Md Imran Hussain
Copyright: © 2021 |Pages: 14
ISBN13: 9781799825661|ISBN10: 1799825663|ISBN13 Softcover: 9781799825678|EISBN13: 9781799825685
DOI: 10.4018/978-1-7998-2566-1.ch010
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MLA

Anwar, Taushif, et al. "Challenges and Applications of Recommender Systems in E-Commerce." Challenges and Applications of Data Analytics in Social Perspectives, edited by V. Sathiyamoorthi and Atilla Elci, IGI Global, 2021, pp. 175-188. https://doi.org/10.4018/978-1-7998-2566-1.ch010

APA

Anwar, T., Uma, V., & Hussain, M. I. (2021). Challenges and Applications of Recommender Systems in E-Commerce. In V. Sathiyamoorthi & A. Elci (Eds.), Challenges and Applications of Data Analytics in Social Perspectives (pp. 175-188). IGI Global. https://doi.org/10.4018/978-1-7998-2566-1.ch010

Chicago

Anwar, Taushif, V. Uma, and Md Imran Hussain. "Challenges and Applications of Recommender Systems in E-Commerce." In Challenges and Applications of Data Analytics in Social Perspectives, edited by V. Sathiyamoorthi and Atilla Elci, 175-188. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-2566-1.ch010

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Abstract

E-commerce and online business are getting too much attention and popularity in this era. A significant challenge is helping a customer through the recommendation of a big list of items to find the one they will like the most efficiently. The most important task of a recommendation system is to improve user experience through the most relevant recommendation of items based on their past behaviour. In e-commerce, the main idea behind the recommender system is to establish the relationship between users and items to recommend the most relevant items to the particular user. Most of the e-commerce websites such as Amazon, Flipkart, E-Bay, etc. are already applying the recommender system to assist their users in finding appropriate items. The main objective of this chapter is to illustrate and examine the issues, attacks, and research applications related to the recommender system.

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