132 citations · 358 across the 17 of their papers we have counts for
17 papers
Disentangled Interest Network for Out-of-Distribution CTR Prediction
Yu Zheng, Chen Gao, Jianxin Chang +5
Click-through rate (CTR) prediction, which estimates the probability of a user clicking on a given item, is a critical task for online information services. Existing approaches oft…
Advancing network resilience theories with symbolized reinforcement learning
Yu Zheng, Jingtao Ding, Depeng Jin +2
Many complex networks display remarkable resilience under external perturbations, internal failures and environmental changes, yet they can swiftly deteriorate into dysfunction upo…
Large-scale Urban Facility Location Selection with Knowledge-informed Reinforcement Learning
Hongyuan Su, Yu Zheng, Jingtao Ding +2
The facility location problem (FLP) is a classical combinatorial optimization challenge aimed at strategically laying out facilities to maximize their accessibility. In this paper,…
Rumor Mitigation in Social Media Platforms with Deep Reinforcement Learning
Hongyuan Su, Yu Zheng, Jingtao Ding +2
Social media platforms have become one of the main channels where people disseminate and acquire information, of which the reliability is severely threatened by rumors widespread i…
MetroGNN: Metro Network Expansion with Reinforcement Learning
Hongyuan Su, Yu Zheng, Jingtao Ding +2
Selecting urban regions for metro network expansion to meet maximal transportation demands is crucial for urban development, while computationally challenging to solve. The expansi…
A Comprehensive Survey on Artificial Intelligence for Complex Network: Potential, Methodology and Application
Jingtao Ding, Chang Liu, Yu Zheng +8
Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve fro…
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…
Road Planning for Slums via Deep Reinforcement Learning
Yu Zheng, Hongyuan Su, Jingtao Ding +2
Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate road infrastructure within slums, and road planning for slums is critical to the sustai…
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…
Mutual Harmony: Sequential Recommendation with Dual Contrastive Network
Guanyu Lin, Chen Gao, Yinfeng Li +6
With the outbreak of today's streaming data, the sequential recommendation is a promising solution to achieve time-aware personalized modeling. It aims to infer the next interacted…
DVR: Micro-Video Recommendation Optimizing Watch-Time-Gain under Duration Bias
Yu Zheng, Chen Gao, Jingtao Ding +4
Recommender systems are prone to be misled by biases in the data. Models trained with biased data fail to capture the real interests of users, thus it is critical to alleviate the…
Causal Inference in Recommender Systems: A Survey and Future Directions
Chen Gao, Yu Zheng, Wenjie Wang +3
Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behaviora…
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…
DGCN: Diversified Recommendation with Graph Convolutional Networks
Yu Zheng, Chen Gao, Liang Chen +2
These years much effort has been devoted to improving the accuracy or relevance of the recommendation system. Diversity, a crucial factor which measures the dissimilarity among the…
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…
Price-aware Recommendation with Graph Convolutional Networks
Yu Zheng, Chen Gao, Xiangnan He +2
In recent years, much research effort on recommendation has been devoted to mining user behaviors, i.e., collaborative filtering, along with the general information which describes…