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The explosion of digital content has necessitated the development of robust recommender systems. Collaborative Filtering (CF) remains the dominant approach, relying on historical user-item interactions to predict future preferences. However, as datasets grow—often exceeding millions of ratings—traditional CF methods struggle with computational complexity and the "long tail" problem of item sparsity.

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