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2407.16357
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TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou
23 July 2024
Zihua Si
Lin Guan
ZhongXiang Sun
Xiaoxue Zang
Jing Lu
Yiqun Hui
Xingchao Cao
Zeyu Yang
Yichen Zheng
Dewei Leng
Kai Zheng
Chenbin Zhang
Ya-Ling Niu
Yang Song
Kun Gai
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Papers citing
"TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou"
6 / 6 papers shown
Title
LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders
Zheng Chai
Qin Ren
Xijun Xiao
H. Yang
Bo Han
...
Xiang Sun
Yaocheng Tan
Peng Xu
Yuchao Zheng
Di Wu
44
0
0
07 May 2025
Towards Large-scale Generative Ranking
Yanhua Huang
Y. Chen
Xiong Cao
Rui Yang
Mingliang Qi
...
L. Chen
Weihang Chen
Min Zhu
Ruiwen Xu
Lei Zhang
39
0
0
07 May 2025
Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction
Xiang Xu
Hao Wang
Wei Guo
L. Zhang
Wanshan Yang
R. Yu
Y. Liu
Defu Lian
Enhong Chen
33
5
0
22 Nov 2024
Long-Sequence Recommendation Models Need Decoupled Embeddings
Ningya Feng
Junwei Pan
Jialong Wu
Baixu Chen
Ximei Wang
Qian Li
Xian Hu
Jie Jiang
Mingsheng Long
AI4TS
37
2
0
03 Oct 2024
ELASTIC: Efficient Linear Attention for Sequential Interest Compression
Jiaxin Deng
Shiyao Wang
Song Lu
Yinfeng Li
Xinchen Luo
Yuanjun Liu
Peixing Xu
Guorui Zhou
34
0
0
18 Aug 2024
Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction
Yue Cao
Xiaojiang Zhou
Jiaqi Feng
Peihao Huang
Yao Xiao
Dayao Chen
Sheng Chen
82
39
0
20 May 2022
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