A STUDY OF RECOMMENDATION SYSTEM IN E-COMMERCE

Abstract

Recommendation Systems (RS) are commonly employed in the e-commerce business to deal with the problem of information overload. Because there is so much information available these days, users are having trouble discovering relevant product and service information that matches their tastes and interests. The technique of obtaining relevant knowledge from enormous databases is known as data mining (DM). DM's job is to describe and forecast data so that information may be retrieved. Information retrieval (IR) is a subfield of RS, which is a subfield of data mining (DM). Recommendation engines are essentially data filtering and information retrieval tools that employ algorithms and data to suggest the most relevant item to a given user. Content-based (CB) filtering, Collaborative Filtering (CF), and hybrid filtering techniques are some of the strategies and methodologies employed by RS. This study explains the function of data mining in recommendation systems and provides an RS process. Also includes a methodological overview, RS difficulties, and a comparison of several e-commerce website recommendation systems