119 citations · 387 across the 14 of their papers we have counts for
14 papers
Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation
Yunzhu Pan, Chen Gao, Jianxin Chang +5
Sequential recommendation is one of the most important tasks in recommender systems, which aims to recommend the next interacted item with historical behaviors as input. Traditiona…
Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System
Yunzhu Pan, Nian Li, Chen Gao +5
Short-video recommendation is one of the most important recommendation applications in today's industrial information systems. Compared with other recommendation tasks, the enormou…
Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework
Yang Zhang, Yimeng Bai, Jianxin Chang +6
With the proliferation of short video applications, the significance of short video recommendations has vastly increased. Unlike other recommendation scenarios, short video recomme…
KuaiSAR: A Unified Search And Recommendation Dataset
Zhongxiang Sun, Zihua Si, Xiaoxue Zang +5
The confluence of Search and Recommendation (S&R) services is vital to online services, including e-commerce and video platforms. The integration of S&R modeling is a highly intuit…
Multi-behavior Self-supervised Learning for Recommendation
Jingcao Xu, Chaokun Wang, Cheng Wu +6
Modern recommender systems often deal with a variety of user interactions, e.g., click, forward, purchase, etc., which requires the underlying recommender engines to fully understa…
When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation
Zihua Si, Zhongxiang Sun, Xiao Zhang +5
Modern online service providers such as online shopping platforms often provide both search and recommendation (S&R) services to meet different user needs. Rarely has there been an…
Dual-interest Factorization-heads Attention for Sequential Recommendation
Guanyu Lin, Chen Gao, Yu Zheng +6
Accurate user interest modeling is vital for recommendation scenarios. One of the effective solutions is the sequential recommendation that relies on click behaviors, but this is n…
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…
When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation
Yu Tian, Jianxin Chang, Yannan Niu +2
Sequential recommendation aims at identifying the next item that is preferred by a user based on their behavioral history. Compared to conventional sequential models that leverage…
Disentangling Long and Short-Term Interests for Recommendation
Yu Zheng, Chen Gao, Jianxin Chang +4
Modeling user's long-term and short-term interests is crucial for accurate recommendation. However, since there is no manually annotated label for user interests, existing approach…
A Model-Agnostic Causal Learning Framework for Recommendation using Search Data
Zihua Si, Xueran Han, Xiao Zhang +4
Machine-learning based recommender systems(RSs) has become an effective means to help people automatically discover their interests. Existing models often represent the rich inform…
Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation
Yiyu Liu, Qian Liu, Yu Tian +4
Recently, micro-video sharing platforms such as Kuaishou and Tiktok have become a major source of information for people's lives. Thanks to the large traffic volume, short video li…
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…