Updates on Movie Recommendation System
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Date
2021-02
Journal Title
Journal ISSN
Volume Title
Publisher
Faculty of Technology Education, Abubakar Tafawa Balewa University Bauchi
Abstract
In recent years, there is a huge number of movies on the
internet. Users have different desires for a movie to watch as
there are different cultures, languages, and genres to choose
from in a movie domain. As a result, a recommendation
system approach is used to suggest the best movies to users
according to their preferences. Several different algorithms
and strategies have been proposed to effectively capture
users’ interest and provide an accurate recommendation of
movies. Memory-Based Collaborative Filtering Recommender
Systems existed for the best part of the last two decades. It is
an advanced technology, implemented in various commercial
applications which because of its effectiveness has been the
predominantly used technique to date in recommendation
system. Memory-based collaborative filtering approach is
popularly and extensively used in practice but yet faces some
key challenges in providing high-quality recommendations
due to the daily increase of items and visitors of different
websites. This paper presents a review of different techniques
and similarity measures used in the movie recommendation
system and also proposed a model that can be used to build
robust, accurate and scalable movie recommendation to
users.
Description
Keywords
Movie recommendation system, Similarity measures, content-based approach, collaborative filtering, mean absolute error