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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Bibliographic Details
Main Authors: Xie, Xu, Sun, Fei, Liu, Zhaoyang, Wu, Shiwen, Gao, Jinyang, Zhang, Jiandong, Ding, Bolin, Cui, Bin
Format: Conference Proceeding
Language:English
Subjects:
Behavioral sciences
Computer vision
Conferences
Contrastive Learning
Data engineering
Data mining
Data models
Deep Learning
Multitasking
Recom-mender Systems
Online Access:Request full text
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