Hybrid contrastive multi-scenario learning for multi-task sequential-dependence recommendation

Multi-scenario and multi-task learning are crucial in industrial recommendation systems to deliver high-quality recommendations across diverse scenarios with minimal computational overhead. However, conventional models often fail to effectively leverage cross-scenario information, limiting their rep...

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Veröffentlicht in:	Neural networks 2025-03, Vol.183, p.106953, Article 106953
Hauptverfasser:	Yi, Qingqing, Wu, Lunwen, Tang, Jingjing, Zeng, Yujian, Song, Zengchun
Format:	Artikel
Sprache:	eng
Schlagworte:	Contrastive learning Multi-scenario learning Multi-task learning Sequential-dependence recommendation
Online-Zugang:	Volltext
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