Social Media (Twitter) Based Movie Recommendation System On Disney+ With Hybrid Filtering Using Neighboor's K-Nearest Method
https://doi.org/10.35877/454RI.jinav1954
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Abstract
The research aims on the development of a film recommendation system that combines the Hybrid Filtering and K-Nearest Neighbors method. (KNN). Hybrid Filtering combines a variety of recommendation techniques, including collaborative filtering and content-based filtering, while KNN is a simple method for classifying data based on similarities with close neighbors. The data set used is Kaggle’s Rotten Tomatoes, which includes information such as critics’ names, genres, movie titles, and review content. The aim of the study was to build an accurate system of recommendations based on user ratings on Disney+ Hotstar and measure its performance using MAE (Mean Absolute Error) and Confusion Matrix assessments.The results showed that the combination of Hybrid Filtering and KNN methods resulted in better accuracy values in giving film recommendations compared to using only the Collaborative Filtering method. Graphics and performance analysis show that the developed models are able to provide film recommendations with increasing accuracy over time. In conclusion, the combination of Hybrid Filtering and K-Nearest Neighbors methods is effective in improving the accuracy of the movie recommendation system, helping users choose films that match their preferences on the Disney+ Hotstar platform. This research contributes to the development of better and more accurate recommendation systems in the film industry.
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