AI and ML Integration Using Collaborative Filtering in Movie Recommendations
DOI:
https://doi.org/10.62123/enigma.v2i2.55Keywords:
Artificial Intelligence, Machine Learning, Collaborative Filtering, Movie Recommendation, Singular Value DecompositionAbstract
This study aims to integrate Artificial Intelligence (AI) and Machine Learning (ML) technologies with Collaborative Filtering (CF) to build a more accurate and personalized movie recommendation system. This system uses the Singular Value Decomposition (SVD) algorithm to reduce the dimensionality of data and generate rating predictions for users of movies they have not watched. This study implements a dataset from MovieLens to test the effectiveness of the model in providing recommendations. The experimental results show that the system successfully predicts user ratings with fairly high accuracy, reflected in the average Root Mean Square Error (RMSE) value of 0.85 for the five users tested. Although these results show good performance, challenges such as cold start problems and data sparsity are still major obstacles in producing more optimal recommendations. Therefore, this study also proposes the use of hybrid filtering, deep learning, and the use of external data to improve prediction accuracy and overcome these limitations.
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