119 citations · 393 across the 22 of their papers we have counts for
14 papers · 1 filter
Inverse Learning with Extremely Sparse Feedback for Recommendation
Guanyu Lin, Chen Gao, Yu Zheng +8
Modern personalized recommendation services often rely on user feedback, either explicit or implicit, to improve the quality of services. Explicit feedback refers to behaviors like…
Mixed Attention Network for Cross-domain Sequential Recommendation
Guanyu Lin, Chen Gao, Yu Zheng +8
In modern recommender systems, sequential recommendation leverages chronological user behaviors to make effective next-item suggestions, which suffers from data sparsity issues, es…
Alleviating Video-Length Effect for Micro-video Recommendation
Yuhan Quan, Jingtao Ding, Chen Gao +4
Micro-videos platforms such as TikTok are extremely popular nowadays. One important feature is that users no longer select interested videos from a set, instead they either watch t…
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…
Uncertainty-aware Consistency Learning for Cold-Start Item Recommendation
Taichi Liu, Chen Gao, Zhenyu Wang +4
Graph Neural Network (GNN)-based models have become the mainstream approach for recommender systems. Despite the effectiveness, they are still suffering from the cold-start problem…
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…
A Probabilistic Fluctuation based Membership Inference Attack for Diffusion Models
Wenjie Fu, Huandong Wang, Liyuan Zhang +3
Membership Inference Attack (MIA) identifies whether a record exists in a machine learning model's training set by querying the model. MIAs on the classic classification models hav…
Detecting Vulnerable Nodes in Urban Infrastructure Interdependent Network
Jinzhu Mao, Liu Cao, Chen Gao +4
Understanding and characterizing the vulnerability of urban infrastructures, which refers to the engineering facilities essential for the regular running of cities and that exist n…
NEON: Living Needs Prediction System in Meituan
Xiaochong Lan, Chen Gao, Shiqi Wen +6
Living needs refer to the various needs in human's daily lives for survival and well-being, including food, housing, entertainment, etc. On life service platforms that connect user…
Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
Yan Wen, Chen Gao, Lingling Yi +3
Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either…
S: Social-network Simulation System with Large Language Model-Empowered Agents
Chen Gao, Xiaochong Lan, Zhihong Lu +5
Social network simulation plays a crucial role in addressing various challenges within social science. It offers extensive applications such as state prediction, phenomena explanat…
How Graph Convolutions Amplify Popularity Bias for Recommendation?
Jiajia Chen, Jiancan Wu, Jiawei Chen +3
Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns. Although improving the overall acc…
Robust Preference-Guided Denoising for Graph based Social Recommendation
Yuhan Quan, Jingtao Ding, Chen Gao +3
Graph Neural Network(GNN) based social recommendation models improve the prediction accuracy of user preference by leveraging GNN in exploiting preference similarity contained in s…
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…