Mitigating the Grey Sheep Problem in Collaborative Filtering via Hybrid Machine Learning Architectures
DOI:
https://doi.org/10.64060/JESTT3i24Keywords:
Machine Learning Algorithms, Movies, Algorithm, Movie Lens, Grey Sheep Users, Hybrid LearningAbstract
In every nation, the major source of entertainment is movies. Worldwide, thousands of people watch movies to feel relaxed and escape from depression. The huge amount of data is an issue that is getting worse in online media. A System is created by using data mining techniques that helps to anticipate future trends, known as a prediction system or filtering system. In this system, the term grey sheep problem refers to users with unique preferences and interests that make it difficult to predict movies. In this research, Machine Learning models are used to identify the grey sheep problem and enhance movie search. A dataset of 15,538 movies was used and different attributes were selected that are publicly available on MovieLens. Then five main data mining techniques were used: Logistic Regression, Support Vector Machine, K-Nearest Neighbour, Naïve Bayes, and Decision Tree. All of the techniques gave their accuracy values, but out of all, Decision Tree gave the highest accuracy of 0.99% for movie prediction for grey sheep users.
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Copyright (c) 2026 Fiza Noreen, Qurat Ul Aen Naqvi, Awais Rasool , Nimra Razzaq , Fatima Abbas, Bira Alam (Author)
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