Musa, Jamilu MaarufZhihong, XuSaminu, SaniMuswelu, CecilliaKaraye, Ibrahim AbdullahiAhmad, Isah Salim2022-01-102022-01-102021-022277-0011https://uilspace.unilorin.edu.ng/handle/20.500.12484/7290In 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.enMovie recommendation systemSimilarity measurescontent-based approachcollaborative filteringmean absolute errorArticle