User-Based Collaborative Filtering for Movie Recommendation: Implementation and Evaluation on the MovieLens Dataset

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Recommendation systems are an indispensable feature in data mining applications today, owing to their ability to facilitate personal discovery in extensive item sets. In this paper, an end-to-end user-basedcollaborative filtering approach is performed using the MovieLens dataset. The implemented pipeline includes validation, preprocessing, analysis, splitting using positive-only leave-one-out sampling, creationof the user-item matrix, calculation of user-user similarities, rating prediction, generation of Top-K recommendations, and ranking performance. This paper does not introduce any new algorithms; rather, itprovides an experimentally reproducible research on factors influencing the performance of the recommendations generated using user-based collaborative filtering, including evaluation methodology,sparsity consideration, number of neighbors, rating normalization, and similarity filtering. The results demonstrate that the highest-scoring configuration of raw ratings obtained HR/Recall@10 = 0.355 andNDCG@10 = 0.216, beating the mean-centered reference configuration while significantly exceeding a random baseline.