Multi-behavior collaborative contrastive learning for sequential recommendation

Abstract Sequential recommendation (SR) predicts the user’s future preferences based on the sequence of interactions. Recently, some methods for SR have utilized contrastive learning to incorporate self-supervised signals into SR to alleviate the data sparsity problem. Despite these achievements, th...

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Autori principali:	Yuzhe Chen, Qiong Cao, Xianying Huang, Shihao Zou
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Springer 2024-04-01
Serie:	Complex & Intelligent Systems
Soggetti:	Sequential recommendation Multi-behavior recommendation Contrastive learning Graph neural network
Accesso online:	https://doi.org/10.1007/s40747-024-01423-1
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