Abstract : A type of solution to the information overload issue experienced by users of websites that allow the rating of specific items is the recommender system. One of the most effective, practical, and well-known applications for people to view movies quickly is called movie recommendation system (MRS). There have been numerous attempts by researchers to overcome these problems, such as using MRS to watch a movie or buy a book, however the majority of these studies have been unsuccessful in addressing the cold start problem, data sparsity, and malicious attacks. In order to solve these issues, this work proposes a hybrid machine learning algorithm to indorse a suitable movie. A non-cold user went through many models with a trust filter, and a cold user produced the best possible score using their own personal preferences.
Cite : Srivastava, A. P., & Sinha, S. K. (2023). Movie Recommendation System Modeling Using Hybrid Classification Approach (1st ed., pp. 158-164). Noble Science Press. https://doi.org/10.52458/9789388996570.2023.eb.ch33
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