Contrastive Learning for Sequential Recommendation

Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical inter-actions. Despite their success, we argue that these approaches usually rely on the sequential prediction task to optimi...

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Veröffentlicht in:	Data engineering S. 1259 - 1273
Hauptverfasser:	Xie, Xu, Sun, Fei, Liu, Zhaoyang, Wu, Shiwen, Gao, Jinyang, Zhang, Jiandong, Ding, Bolin, Cui, Bin
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.01.2022
Schlagworte:	Behavioral sciences Computer vision Conferences Contrastive Learning Data engineering Data mining Data models Deep Learning Multitasking Recom-mender Systems
ISSN:	2375-026X
Online-Zugang:	Volltext
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