2.4k citations · 7.9k across the 182 of their papers we have counts for
9 papers · 1 filter
Deep reinforcement learning for search, recommendation, and online advertising: a survey
Xiangyu Zhao, Long Xia, Jiliang Tang +1
Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web. These information seeking techniques, satisfying users' inf…
Streaming Graph Neural Networks
Yao Ma, Ziyi Guo, Zhaochun Ren +3
Graphs are essential representations of many real-world data such as social networks. Recent years have witnessed the increasing efforts made to extend the neural network models to…
Signed Graph Convolutional Network
Tyler Derr, Yao Ma, Jiliang Tang
Due to the fact much of today's data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. One recent direction that has show…
Linked Recurrent Neural Networks
Zhiwei Wang, Yao Ma, Dawei Yin +1
Recurrent Neural Networks (RNNs) have been proven to be effective in modeling sequential data and they have been applied to boost a variety of tasks such as document classification…
Multi-dimensional Graph Convolutional Networks
Yao Ma, Suhang Wang, Charu C. Aggarwal +2
Convolutional neural networks (CNNs) leverage the great power in representation learning on regular grid data such as image and video. Recently, increasing attention has been paid…
Crime in Urban Areas: A Data Mining Perspective
Xiangyu Zhao, Jiliang Tang
Urban safety and security play a crucial role in improving life quality of citizen and the sustainable development of urban. Traditional urban crime research focused on leveraging…
Deep Reinforcement Learning for Page-wise Recommendations
Xiangyu Zhao, Long Xia, Liang Zhang +3
Recommender systems can mitigate the information overload problem by suggesting users' personalized items. In real-world recommendations such as e-commerce, a typical interaction b…
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
Xiangyu Zhao, Liang Zhang, Zhuoye Ding +3
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional reco…
Deep Reinforcement Learning for List-wise Recommendations
Xiangyu Zhao, Liang Zhang, Long Xia +3
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional reco…