33 citations · 57 across the 5 of their papers we have counts for
5 papers
RecFlow: An Industrial Full Flow Recommendation Dataset
Qi Liu, Kai Zheng, Rui Huang +15
Industrial recommendation systems (RS) rely on the multi-stage pipeline to balance effectiveness and efficiency when delivering items from a vast corpus to users. Existing RS bench…
TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou
Zihua Si, Lin Guan, ZhongXiang Sun +12
The significance of modeling long-term user interests for CTR prediction tasks in large-scale recommendation systems is progressively gaining attention among researchers and practi…
TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou
Jianxin Chang, Chenbin Zhang, Zhiyi Fu +8
Life-long user behavior modeling, i.e., extracting a user's hidden interests from rich historical behaviors in months or even years, plays a central role in modern CTR prediction s…
PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information
Jianxin Chang, Chenbin Zhang, Yiqun Hui +4
With the increase of content pages and interactive buttons in online services such as online-shopping and video-watching websites, industrial-scale recommender systems face challen…
Sequential Recommendation with Graph Neural Networks
Jianxin Chang, Chen Gao, Yu Zheng +5
Sequential recommendation aims to leverage users' historical behaviors to predict their next interaction. Existing works have not yet addressed two main challenges in sequential re…