A Comprehensive Review on the Recommender System Design in an E-Commerce Website Based on a Customer Review System with the Classification Model

The increase in web data leads to abundant information in the data wealth through different methodologies used in sparse of time. The evaluation is based on achieving profit based on the purchase of the products. The recommendation system (RS) comprises automated strategies to provide appropriate suggestions to the customers with appropriate interaction for the vast range of data. On the other hand, the e-commerce business is a significant player in different scenarios such as photos, magazines, films, and songs for the suggestions such as weddings, financial services, and so on. This paper presents a review of the recommender system for e-commerce applications. The article focuses on the review of customer reviews on e-commerce websites. The e-commerce website provides significant contributions and advancement in product purchases. The purchase intention of the product in e-commerce websites relies on the review of customers. Hence, it is necessary to adopt an effective recommender system based on the trust level of customers. The analysis is based on an examination of the recommendation system (RS) revolution, taxonomy, classification, and dataset used. The extensive review presents existing literature under different classification models for recommendation system (RS).