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• W4387079088 abstract "Recommender System (RS) has generated widespread attention with the aim of expanding different items. Among graph-based recommendation methods, the tripartite graph can better manage data sparsity and cold start, while improving the metrics of various recommendations such as recall, precision, and diversity. Existing tripartite graph-based methods encounter numerous challenges, including mitigating data sparsity, improving diversity, and capturing potential user preferences via social relations. To address these challenges, a Conditional Random Field based on Tripartite Graph (CRF-TG) is proposed. The tripartite graph consists of the user, item, and trust level. The method can mine potentially similar users, create probabilistic models based on TG, and uncover potential user preferences. Moreover, to mine the users with similar preferences outside the social relationship, the random walk method is used to test CRF-TG. Experiments are designed to verify the validity of CRF-TG. Compared to the others considered methods, CRF-TG gives a 15% increase on average in performance indicators such as diversity, recall, and F1." @default.
• W4387079088 created "2023-09-28" @default.
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• W4387079088 date "2024-03-01" @default.
• W4387079088 modified "2023-10-16" @default.
• W4387079088 title "A conditional random field recommendation method based on tripartite graph" @default.
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• W4387079088 doi "https://doi.org/10.1016/j.eswa.2023.121804" @default.
• W4387079088 hasPublicationYear "2024" @default.
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