Abstract:
In today's technical era, every startup or a company attempt to establish a better sort of
communication between their products and the users, and for that purpose, they require a
type of methods which can promote their product effectively, and here the recommender
system serves this motive with positivity. we all know numerous attempts have been made
for expanding the accuracy in recommendation system, but somehow recommendation
scenarios are much more complex and most of the cases have limited rating system for
their items. Here, we present a demonstrative approach that will show how memory and
model-based collaborative filtering enhance the accuracy and efficiently in our proposed
recommendation system. As we know recommendation systems are used in many various
areas’ like music, movie, news, books, social media platform. It is a filtering system that
tries to predict and show the items that a user would like to purchase. In this paper we are
using Memory based (Item to Item) and Model based (Item to User) collaborative filtering
to solve various problem like cold start, grey sheep, data sparsity using Utility Matrix
method which will help to find user item relationship from previous purchase history and
cluster the Item to item relationship using K-means Algorithm which will solve the
problem of cold start, grey sheep and spared the data problem.
