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Aims/ objectives: This paper presents the different types of recommender ﬁltering techniques. The main objective of the study is to provide a review of classical methods used in recommender systems such as collaborative ﬁltering, content-based ﬁltering and hybrid ﬁltering, highlighting the main advantages and limitations. This paper also discusses the state-of-art machine learning based recommendation models including Clustering models and Bayesian Classiﬁers. Further, we discuss the widespread application of recommender systems to a variety of areas such as e-learning and e-news. Finally, the paper evaluates the performance of matrix factorization-based models, nearest neighbours algorithms and co-clustering algorithms in terms of different metrics.
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