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• W4323527093 abstract "An HDI tensor modeling a dynamic network contains rich knowledge regarding dynamic network evolution. An LFT-based model has attracted plenty of attention on extracting useful knowledge form an HDI tensor. However, existing LFT-based models lack solid consideration for the volatility of dynamic network data, thereby leading to the descent of model representation learning ability. To tackle this problem, this chapter proposes a multiple biases-incorporated latent factorization of tensors (MBLFT) model, which incorporates preprocessing bias, short-term bias and long-term bias into an LFT-based model. Empirical studies on two large-scale dynamic networks generated by industrial applications show that the proposed MBLFT model achieves higher prediction accuracy than state-of-the-art models in solving missing link prediction task." @default.
• W4323527093 created "2023-03-09" @default.
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• W4323527093 date "2023-01-01" @default.
• W4323527093 modified "2023-09-27" @default.
• W4323527093 title "Multiple Biases-Incorporated Latent Factorization of Tensors" @default.
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• W4323527093 doi "https://doi.org/10.1007/978-981-19-8934-6_2" @default.
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