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Multi-Granularity and Multi-Interest Contrast-Enhanced Hypergraph Convolutional Networks for Session Recommendation

Aiming at the problems of the incomplete recommendation and sparsity of session data in session recommendation, a new multi-granularity and multi-interest contrast-enhanced hypergraph convolutional network (MGMI-CEHCN) model for session recommendation is proposed. The session data are modeled as a h...

Whakaahuatanga katoa

I tiakina i:
Ngā taipitopito rārangi puna kōrero
Ngā kaituhi matua: Xingbin Mao, Liang Li, Jiaxing He
Hōputu: Artigo
Reo:Inglês
I whakaputaina: MDPI AG 2024-09-01
Rangatū:Applied Sciences
Ngā marau:
Urunga tuihono:https://www.mdpi.com/2076-3417/14/18/8293
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