ISSUES IN VARIOUS RECOMMENDER SYSTEM IN E-COMMERCE – A SURVEY

Abstract

The replacement of traditional shopping fashion by the various modes of online shopping in real-time. Because of traditional shopping, most of them are getting into real feel about the product whichever they buy. The product features will be manually realized by the consumers whereas in online shopping all the consumers believe the descriptive summary of the products and the various factors based on the sold historical data. Now a day’s modern shopping method is moving gradually towards hitting more number of customers. Here recommendation system playing a vital role in suggesting the product by considering the earlier records and increasing the demand. Many of the consumers are attracted by factors like deals on an item, rating, review, and cost of the product. Through these factors, most of the consumers are attracted to taking online shopping instead of traditional shopping methods. For suggesting the products to consumers, many kinds of recommendation algorithms are applied using machine learning and deep learning technology to train the system automatically by observing the customer behavior patterns. But the believing factors of the product will be forged some time; in such cases, consumers are not satisfied with their expectations. The overall survey of this paper will address the research gap and opportunities with the recommendation system.