Product Recommendations Using Textual Similarity Based Learning Models

Recommendation systems are achieving great success in e-Commerce applications, during a live interaction with a customer; recommendation system may apply different techniques to solve the problem of making a correct and relevant product recommendation. The main objective of this research is to perform product recommendation using textual similarity based Learning model. In this research data acquired through Amazon product advertising API after Data cleaning and text preprocessing the content based product recommendation have been performed using Bag of Words(BOW) and Term Frequency-Inverse Document Frequency(TF-IDF) based text vectorization techniques. Textual Description of the product converted into n-dimensional vector, and later the Euclidean similarity can be measured between the ndimensional vector of the queried product and other products. Text-based product similarity through text vectorization technique is very useful in performing content-based product recommendation and recommending the similar item to the user it can be used in various E-Commerce applications since these applications are heavily populated with the textual description of the product. Bag of words and TF-IDF generates an n-dimensional vector of a different textual description of the product which in turn leads to a better recommendation of the product. Experimental results and analysis section clearly describes how the proposed model for text-based product find the similarity of products with queried product and display as output the best-recommended product.