Updates on Movie Recommendation System

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dc.contributor.authorMusa, Jamilu Maaruf
dc.contributor.authorZhihong, Xu
dc.contributor.authorSaminu, Sani
dc.contributor.authorMuswelu, Cecillia
dc.contributor.authorKaraye, Ibrahim Abdullahi
dc.contributor.authorAhmad, Isah Salim
dc.date.accessioned2022-01-10T10:55:22Z
dc.date.available2022-01-10T10:55:22Z
dc.date.issued2021-02
dc.description.abstractIn 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.en_US
dc.identifier.issn2277-0011
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/7290
dc.language.isoenen_US
dc.publisherFaculty of Technology Education, Abubakar Tafawa Balewa University Bauchien_US
dc.subjectMovie recommendation systemen_US
dc.subjectSimilarity measuresen_US
dc.subjectcontent-based approachen_US
dc.subjectcollaborative filteringen_US
dc.subjectmean absolute erroren_US
dc.titleUpdates on Movie Recommendation Systemen_US
dc.typeArticleen_US

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