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Recommender systems are pivotal to the user experience in modern digital platforms. This paper presents a new algorithmic framework designed to address the scalability and sparsity challenges inherent in large-scale datasets. We focus our evaluation on the standard industry benchmark, the MovieLens dataset (specifically the subset containing roughly 17 million ratings ). By optimizing matrix factorization techniques, our proposed model demonstrates a significant reduction in computation time while maintaining competitive Root Mean Square Error (RMSE) scores compared to existing state-of-the-art baselines.

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