Abstract: Semi-supervised symmetric non-negative matrix factorization (SNMF) utilizes the available supervisory information (usually in the form of pairwise constraints) to improve the clustering ...
Abstract: To address the challenges of data sparsity, cold start, and insufficient dynamic adaptability in traditional recommendation systems, this study proposes a personalized recommendation model ...
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