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Top-N Nearest Neighbourhood based Movie Recommendation System using different Recommendation Techniques

Shaikh, Muhammad Imran (2020) Top-N Nearest Neighbourhood based Movie Recommendation System using different Recommendation Techniques. Masters thesis, Dublin, National College of Ireland.

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Background: Recommendations engines are extremely common and utilized by many tech giants like Facebook, Google, IMDB, Netflix, financial services, and many other companies. The task is to build a Top-N Nearest Neighborhood-based movie recommender system using the recommendation techniques such as Contentbased filtering, Collaborative filtering, and Matrix factorization.
Objective: This research project aims to build a recommender system that will recommend movies to the user just not only by predicting rating but also on the similarity basis of similar users and their interested items. The recommender engine will find out the nearest neighbors around that specific user by matching the similarity of items and ratings for the items given by those nearest neighbors. MovieLens dataset(ml-latest- small) is considered for this research which contains movie, ratings CSV files. Five models have been used i.e. KNNBaseline, KNNWithMeans(UB and IB), SVD, and SVDpp. Two cross-validation techniques have been utilized K-Fold CV and LOO(Leave One Out)CV to attain the best accuracy value from multiple machine learning models. Two accuracy measures have been considered which are RMSE and MAE.
Results: The best RMSE accuracy score result is achieved with the SVDpp algorithm by implementing it on the matrix factorization recommendation technique, the final accuracy mean of the model was almost 88% RMSE with 66% MAE score from K-Fold cross validator while 91% RMSE score 69% from LOOCV cross validator.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 21 Jan 2021 10:46
Last Modified: 21 Jan 2021 10:46

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