TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou

The significance of modeling long-term user interests for CTR prediction tasks in large-scale recommendation systems is progressively gaining attention among researchers and practitioners. Existing work, such as SIM and TWIN, typically employs a two-stage approach to model long-term user behavior se...

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Veröffentlicht in:	arXiv.org 2024-08
Hauptverfasser:	Si, Zihua, Guan, Lin, Sun, ZhongXiang, Zang, Xiaoxue, Lu, Jing, Hui, Yiqun, Cao, Xingchao, Yang, Zeyu, Zheng, Yichen, Leng, Dewei, Zheng, Kai, Zhang, Chenbin, Niu, Yanan, Yang, Song, Gai, Kun
Format:	Artikel
Sprache:	eng
Schlagworte:	Attention Cluster analysis Clustering Clusters Computer Science - Artificial Intelligence Computer Science - Information Retrieval Effectiveness Modelling Recommender systems Searching User behavior
Online-Zugang:	Volltext
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