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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. The exact alphanumeric sequence does not correspond to

In this paper, we introduce a optimization method tailored for high-volume datasets. We utilize the widely recognized benchmark dataset (often denoted in shorthand by its volume metrics, e.g., the 17 million rating threshold) to validate our approach. Recommender systems are pivotal to the user experience

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