b'Xiangyu Zhao'

research

∙ 09/20/2023

Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting

With the acceleration of urbanization, traffic forecasting has become an...

0 Qian Ma, et al. ∙

research

∙ 09/18/2023

PromptST: Prompt-Enhanced Spatio-Temporal Multi-Attribute Prediction

In the era of information explosion, spatio-temporal data mining serves ...

0 Zijian Zhang, et al. ∙

research

∙ 09/12/2023

HAMUR: Hyper Adapter for Multi-Domain Recommendation

Multi-Domain Recommendation (MDR) has gained significant attention in re...

0 Xiaopeng Li, et al. ∙

research

∙ 09/05/2023

Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation

Click-Through Rate (CTR) prediction is a fundamental technique in recomm...

0 Jingtong Gao, et al. ∙

research

∙ 09/02/2023

AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation

Accurate automatic segmentation of medical images typically requires lar...

0 Xiangyu Zhao, et al. ∙

research

∙ 08/23/2023

Will More Expressive Graph Neural Networks do Better on Generative Tasks?

Graph generation poses a significant challenge as it involves predicting...

0 Xiandong Zou, et al. ∙

research

∙ 08/14/2023

AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation

In the domain of streaming recommender systems, conventional methods for...

0 Ziru Liu, et al. ∙

research

∙ 07/10/2023

CT-based Subchondral Bone Microstructural Analysis in Knee Osteoarthritis via MR-Guided Distillation Learning

Background: MR-based subchondral bone effectively predicts knee osteoart...

0 Yuqi Hu, et al. ∙

research

∙ 07/05/2023

Recommender Systems in the Era of Large Language Models (LLMs)

With the prosperity of e-commerce and web applications, Recommender Syst...

0 Wenqi Fan, et al. ∙

research

∙ 06/08/2023

Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs

Graphs are widely used to encapsulate a variety of data formats, but rea...

0 Zehui Li, et al. ∙

research

∙ 04/23/2023

Diffusion Model for GPS Trajectory Generation

With the deployment of GPS-enabled devices and data acquisition technolo...

0 Yuanshao Zhu, et al. ∙

research

∙ 04/20/2023

Domain Generalization for Mammographic Image Analysis via Contrastive Learning

Mammographic image analysis is a fundamental problem in the computer-aid...

0 Zheren Li, et al. ∙

research

∙ 04/16/2023

AutoSTL: Automated Spatio-Temporal Multi-Task Learning

Spatio-Temporal prediction plays a critical role in smart city construct...

0 Zijian Zhang, et al. ∙

research

∙ 04/07/2023

Continuous Input Embedding Size Search For Recommender Systems

Latent factor models are the most popular backbones for today's recommen...

0 Yunke Qu, et al. ∙

research

∙ 03/20/2023

IMF: Interactive Multimodal Fusion Model for Link Prediction

Link prediction aims to identify potential missing triples in knowledge ...

0 Xinhang Li, et al. ∙

research

∙ 03/12/2023

AutoDenoise: Automatic Data Instance Denoising for Recommendations

Historical user-item interaction datasets are essential in training mode...

0 Weilin Lin, et al. ∙

research

∙ 03/11/2023

User Retention-oriented Recommendation with Decision Transformer

Improving user retention with reinforcement learning (RL) has attracted ...

0 Kesen Zhao, et al. ∙

research

∙ 03/11/2023

AutoMLP: Automated MLP for Sequential Recommendations

Sequential recommender systems aim to predict users' next interested ite...

0 Muyang Li, et al. ∙

research

∙ 02/12/2023

Denoising and Prompt-Tuning for Multi-Behavior Recommendation

In practical recommendation scenarios, users often interact with items u...

0 Chi Zhang, et al. ∙

research

∙ 02/08/2023

Multimodal Recommender Systems: A Survey

The recommender system (RS) has been an integral toolkit of online servi...

0 Qidong Liu, et al. ∙

research

∙ 02/07/2023

Multi-Task Deep Recommender Systems: A Survey

Multi-task learning (MTL) aims at learning related tasks in a unified mo...

0 Yuhao Wang, et al. ∙

research

∙ 02/07/2023

Multi-Task Recommendations with Reinforcement Learning

In recent years, Multi-task Learning (MTL) has yielded immense success i...

0 Ziru Liu, et al. ∙

research

∙ 01/13/2023

RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation

Medical image segmentation methods are generally designed as fully-super...

0 Xiangyu Zhao, et al. ∙

research

∙ 12/20/2022

Data Augmentation on Graphs: A Survey

In recent years, graph representation learning has achieved remarkable s...

0 Jiajun Zhou, et al. ∙

research

∙ 09/21/2022

A Comprehensive Survey on Trustworthy Recommender Systems

As one of the most successful AI-powered applications, recommender syste...

0 Wenqi Fan, et al. ∙

research

∙ 08/16/2022

"Are you okay, honey?": Recognizing Emotions among Couples Managing Diabetes in Daily Life using Multimodal Real-World Smartwatch Data

Couples generally manage chronic diseases together and the management ta...

0 George Boateng, et al. ∙

research

∙ 08/12/2022

TBI-GAN: An Adversarial Learning Approach for Data Synthesis on Traumatic Brain Segmentation

Brain network analysis for traumatic brain injury (TBI) patients is crit...

10 Xiangyu Zhao, et al. ∙

research

∙ 07/24/2022

Federated Graph Contrastive Learning

Graph learning models are critical tools for researchers to explore grap...

0 Haoran Yang, et al. ∙

research

∙ 07/21/2022

Knowledge-enhanced Black-box Attacks for Recommendations

Recent studies have shown that deep neural networks-based recommender sy...

0 Jingfan Chen, et al. ∙

research

∙ 04/25/2022

MLP4Rec: A Pure MLP Architecture for Sequential Recommendations

Self-attention models have achieved state-of-the-art performance in sequ...

0 Muyang Li, et al. ∙

research

∙ 04/19/2022

AutoField: Automating Feature Selection in Deep Recommender Systems

Feature quality has an impactful effect on recommendation performance. T...

0 Yejing Wang, et al. ∙

research

∙ 04/04/2022

Automated Machine Learning for Deep Recommender Systems: A Survey

Deep recommender systems (DRS) are critical for current commercial onlin...

0 Bo Chen, et al. ∙

research

∙ 02/25/2022

Building a 3-Player Mahjong AI using Deep Reinforcement Learning

Mahjong is a popular multi-player imperfect-information game developed i...

0 Xiangyu Zhao, et al. ∙

research

∙ 08/12/2021

Graph Trend Networks for Recommendations

Recommender systems aim to provide personalized services to users and ar...

13 Wenqi Fan, et al. ∙

research

∙ 07/19/2021

Structural Watermarking to Deep Neural Networks via Network Channel Pruning

In order to protect the intellectual property (IP) of deep neural networ...

0 Xiangyu Zhao, et al. ∙

research

∙ 06/12/2021

AutoLoss: Automated Loss Function Search in Recommendations

Designing an effective loss function plays a crucial role in training de...

11 Xiangyu Zhao, et al. ∙

research

∙ 06/10/2021

Automated Self-Supervised Learning for Graphs

Graph self-supervised learning has gained increasing attention due to it...

10 Wei Jin, et al. ∙

research

∙ 05/04/2021

Data-Efficient Reinforcement Learning for Malaria Control

Sequential decision-making under cost-sensitive tasks is prohibitively d...

0 Lixin Zou, et al. ∙

research

∙ 02/10/2021

D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism

Coronavirus Disease 2019 (COVID-19) has caused great casualties and beco...

0 Xiangyu Zhao, et al. ∙

research

∙ 11/01/2020

Watermarking Graph Neural Networks by Random Graphs

Many learning tasks require us to deal with graph data which contains ri...

0 Xiangyu Zhao, et al. ∙

research

∙ 07/04/2020

Neural Interactive Collaborative Filtering

In this paper, we study collaborative filtering in an interactive settin...

0 Lixin Zou, et al. ∙

research

∙ 06/26/2020

Memory-efficient Embedding for Recommendations

Practical large-scale recommender systems usually contain thousands of f...

1 Xiangyu Zhao, et al. ∙

research

∙ 05/17/2020

Attacking Black-box Recommendations via Copying Cross-domain User Profiles

Recently, recommender systems that aim to suggest personalized lists of ...

0 Wenqi Fan, et al. ∙

research

∙ 02/28/2020

Jointly Learning to Recommend and Advertise

Online recommendation and advertising are two major income channels for ...

1 Xiangyu Zhao, et al. ∙

research

∙ 02/26/2020

AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations

Deep learning based recommender systems (DLRSs) often have embedding lay...

1 Xiangyu Zhao, et al. ∙

research

∙ 01/20/2020

Exploring Spatio-Temporal and Cross-Type Correlations for Crime Prediction

Crime prediction plays an impactful role in enhancing public security an...

0 Xiangyu Zhao, et al. ∙

research

∙ 09/09/2019

Deep Reinforcement Learning for Online Advertising in Recommender Systems

With the recent prevalence of Reinforcement Learning (RL), there have be...

0 Xiangyu Zhao, et al. ∙

research

∙ 06/27/2019

Toward Simulating Environments in Reinforcement Learning Based Recommendations

With the recent advances in Reinforcement Learning (RL), there have been...

0 Xiangyu Zhao, et al. ∙

research

∙ 02/11/2019

Model-Based Reinforcement Learning for Whole-Chain Recommendations

With the recent prevalence of Reinforcement Learning (RL), there have be...

0 Xiangyu Zhao, et al. ∙

research

∙ 12/18/2018

Deep Reinforcement Learning for Search, Recommendation, and Online Advertising: A Survey

Search, recommendation, and advertising are the three most important inf...

0 Xiangyu Zhao, et al. ∙

Xiangyu Zhao

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