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Product Recommendation Using Machine Learning a Review of the Existing Techniques


Anam Naz Sodhar, Umair Ali Khan, Irum Naz Sodhar, Abdul Hafeez Buller, and Jahanzeb Sodhar


Vol. 22  No. 5  pp. 523-530


The popularity of Product-Recommendation (PR) or system of recommendation is rising day by day. Product suggestions are an-ecommerce customization approach which goods are continuously created for a customer on a webpage, application, or email based on data such user characteristics, browsing behavior, or situational context, resulting in an individualized purchasing experience. The system of recommendation used to predict or recommend the product according to the taste of customer. In today’s life product recommendation system has been used by different E-Commerce sites. A website that allows people to buy and sell physical things, services, and digital products without having to go to a physical store. Through an e-commerce website, a company can manage orders, payments, shipping and logistics, and customer service. Recommendation can be of any type such as for music recommendation there is Spotify, for movies Netflix, for videos YouTube, play store (for different categories) and so on. For the recommendation of product different filtering methods and algorithms were used, to recommend products according to user’s likeness. In this paper discussed about the existing Machine Learning Techniques (MLT) which were used for the product recommendation. Through these techniques the algorithm is used to predict or similar items according to user’s likeness based on his information.


Product Recommendation; Machine Learning Techniques; Recommendation system; categories; Algorithms