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Machine Learning Techniques to Recommend Products in E-Commerce: A Systematic Review

International Journal on Customer Relations

Volume 9 Issue 1 & 2

Published: 2021
Author(s) Name: Md Hesam Akhter, Vinod Kumar Panchal | Author(s) Affiliation: Al-Falah University, Haryana, India.
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Abstract

Product recommender systems have been an effective approach to overcoming information overload on the Web, with the growing size of online statistics, as recommending the right product based on consumer liking became challenging for e-commerce businesses. The machine learning techniques can be applied to solve it. However, due to the large number of algorithms available in the literature, it is quite difficult to select a suitable machine learning algorithm. Researchers have little information about the best approaches to develop recommender systems for e-commerce using machine learning. Here, we have presented our work as a systematic review of the literature, which surveys to choose machine learning algorithms to recommend products in e-commerce and recognise research opportunities for the researchers in developing recommender systems. The survey concluded that deep learning and neural networks techniques are widely used to predict the right products for recommendation to the customers in e-commerce, because they can be very good at recognising patterns in a way similar to the human brain.

Keywords: Machine Learning, Product Recommendation, E-Commerce, Systematic Review

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