Authors :
Shivam Sharma; Shikhar Gaur; Shubham Shukla; Vibhav kumar Sacha
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://shorturl.at/bHN78
DOI :
https://doi.org/10.5281/zenodo.7962504
Abstract :
A recommendation engine is a type of data
filtering technology that uses machine learning
techniques to provide the most relevant
recommendations to a particular user or client. It
operates by searching for patterns in client behavior
data, which may be collected explicitly or implicitly. It
first records a client's area of interest and then uses that
information to enable product recommendations for
customers who appear tobe shopping. For example: An
e-commerce website won't know anything about a visitor
if they are completely new to it. So how would the
positioning strategy advocate the product to the
consumer in this situation? One practicalsolution may be
to suggest the item that is in great demand, or the one
that is popular. Another practical option is to
recommend the product that will likely bring more
profit to the company. Recommendation engines can be
implemented by using 3 strategies: - Collaborative
filtering (focuses on collecting and analyzing data about
user behavior, preferences, and activities to predict what
a person would like based on how they are similar to
other users.), content-based filtering (which works on the
principle that if you like one thing, you'll like this other
thing, too) and hybrid models.
Keywords :
Recommendation System,Collaborative Filtering Approach, And Content- Based Filtering Method.
A recommendation engine is a type of data
filtering technology that uses machine learning
techniques to provide the most relevant
recommendations to a particular user or client. It
operates by searching for patterns in client behavior
data, which may be collected explicitly or implicitly. It
first records a client's area of interest and then uses that
information to enable product recommendations for
customers who appear tobe shopping. For example: An
e-commerce website won't know anything about a visitor
if they are completely new to it. So how would the
positioning strategy advocate the product to the
consumer in this situation? One practicalsolution may be
to suggest the item that is in great demand, or the one
that is popular. Another practical option is to
recommend the product that will likely bring more
profit to the company. Recommendation engines can be
implemented by using 3 strategies: - Collaborative
filtering (focuses on collecting and analyzing data about
user behavior, preferences, and activities to predict what
a person would like based on how they are similar to
other users.), content-based filtering (which works on the
principle that if you like one thing, you'll like this other
thing, too) and hybrid models.
Keywords :
Recommendation System,Collaborative Filtering Approach, And Content- Based Filtering Method.
