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.
