Search Results for author: Xiangyu Zhao

Found 51 papers, 18 papers with code

Tensorized Hypergraph Neural Networks

no code implementations5 Jun 2023 Maolin Wang, Yaoming Zhen, Yu Pan, Zenglin Xu, Ruocheng Guo, Xiangyu Zhao

Hypergraph neural networks (HGNN) have recently become attractive and received significant attention due to their excellent performance in various domains.

IFedRec: Item-Guided Federated Aggregation for Cold-Start

no code implementations22 May 2023 Chunxu Zhang, Guodong Long, Tianyi Zhou, Xiangyu Zhao, Zijian Zhang, Bo Yang

To recommend cold items, existing federated recommendation models require collecting new interactions from users and retraining the model, which is time-consuming and poses a privacy threat to users' sensitive information.

Diffusion Model for GPS Trajectory Generation

no code implementations23 Apr 2023 Yuanshao Zhu, Yongchao Ye, Xiangyu Zhao, James J. Q. Yu

With the deployment of GPS-enabled devices and data acquisition technology, the massively generated GPS trajectory data provide a core support for advancing spatial-temporal data mining research.

Denoising Noise Level Prediction

Domain Generalization for Mammographic Image Analysis via Contrastive Learning

no code implementations20 Apr 2023 Zheren Li, Zhiming Cui, Lichi Zhang, Sheng Wang, Chenjin Lei, Xi Ouyang, Dongdong Chen, Zixu Zhuang, Xiangyu Zhao, Yajia Gu, Zaiyi Liu, Chunling Liu, Dinggang Shen, Jie-Zhi Cheng

Mammographic image analysis is a fundamental problem in the computer-aided diagnosis scheme, which has recently made remarkable progress with the advance of deep learning.

breast density classification Contrastive Learning +2

AutoSTL: Automated Spatio-Temporal Multi-Task Learning

no code implementations16 Apr 2023 Zijian Zhang, Xiangyu Zhao, Hao Miao, Chunxu Zhang, Hongwei Zhao, Junbo Zhang

To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly.

Multi-Task Learning

Continuous Input Embedding Size Search For Recommender Systems

no code implementations7 Apr 2023 Yunke Qu, Tong Chen, Xiangyu Zhao, Lizhen Cui, Kai Zheng, Hongzhi Yin

Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance.

Recommendation Systems Reinforcement Learning (RL)

IMF: Interactive Multimodal Fusion Model for Link Prediction

1 code implementation20 Mar 2023 Xinhang Li, Xiangyu Zhao, Jiaxing Xu, Yong Zhang, Chunxiao Xing

To this end, we propose a two-stage multimodal fusion framework to preserve modality-specific knowledge as well as take advantage of the complementarity between different modalities.

Contrastive Learning Knowledge Graphs +1

AutoDenoise: Automatic Data Instance Denoising for Recommendations

no code implementations12 Mar 2023 Weilin Lin, Xiangyu Zhao, Yejing Wang, Yuanshao Zhu, Wanyu Wang

In the searching phase, we aim to train the policy network with the capacity of instance denoising; in the validation phase, we find out and evaluate the denoised subset of data instances selected by the trained policy network, so as to validate its denoising ability.

Denoising Recommendation Systems

AutoMLP: Automated MLP for Sequential Recommendations

no code implementations11 Mar 2023 Muyang Li, Zijian Zhang, Xiangyu Zhao, Wanyu Wang, Minghao Zhao, Runze Wu, Ruocheng Guo

Sequential recommender systems aim to predict users' next interested item given their historical interactions.

Recommendation Systems

User Retention-oriented Recommendation with Decision Transformer

1 code implementation11 Mar 2023 Kesen Zhao, Lixin Zou, Xiangyu Zhao, Maolin Wang, Dawei Yin

However, deploying the DT in recommendation is a non-trivial problem because of the following challenges: (1) deficiency in modeling the numerical reward value; (2) data discrepancy between the policy learning and recommendation generation; (3) unreliable offline performance evaluation.

Contrastive Learning Reinforcement Learning (RL)

Denoising and Prompt-Tuning for Multi-Behavior Recommendation

1 code implementation12 Feb 2023 Chi Zhang, Rui Chen, Xiangyu Zhao, Qilong Han, Li Li

In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e. g., click, add-to-cart, and purchase).

Collaborative Filtering Denoising

Multimodal Recommender Systems: A Survey

1 code implementation8 Feb 2023 Qidong Liu, Jiaxi Hu, Yutian Xiao, Jingtong Gao, Xiangyu Zhao

In this paper, we will give a comprehensive survey of the MRS models, mainly from technical views.

Model Optimization Recommendation Systems

Multi-Task Recommendations with Reinforcement Learning

1 code implementation7 Feb 2023 Ziru Liu, Jiejie Tian, Qingpeng Cai, Xiangyu Zhao, Jingtong Gao, Shuchang Liu, Dayou Chen, Tonghao He, Dong Zheng, Peng Jiang, Kun Gai

To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks.

Multi-Task Learning Recommendation Systems +2

Exploration and Regularization of the Latent Action Space in Recommendation

1 code implementation7 Feb 2023 Shuchang Liu, Qingpeng Cai, Bowen Sun, Yuhao Wang, Ji Jiang, Dong Zheng, Kun Gai, Peng Jiang, Xiangyu Zhao, Yongfeng Zhang

To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step.

Recommendation Systems

Multi-Task Deep Recommender Systems: A Survey

no code implementations7 Feb 2023 Yuhao Wang, Ha Tsz Lam, Yi Wong, Ziru Liu, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, Ruiming Tang

Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge.

Multi-Task Learning Recommendation Systems

Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration

1 code implementation27 Jan 2023 Xiangyu Zhao, Hannes Stärk, Dominique Beaini, Yiren Zhao, Pietro Liò

Existing GNN benchmarking methods for molecular representation learning focus on comparing the GNNs' performances on some node/graph classification/regression tasks on certain datasets.

Benchmarking Graph Classification +2

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

1 code implementation13 Jan 2023 Xiangyu Zhao, Zengxin Qi, Sheng Wang, Qian Wang, Xuehai Wu, Ying Mao, Lichi Zhang

However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches.

Contrastive Learning Image Segmentation +2

Data Augmentation on Graphs: A Technical Survey

1 code implementation20 Dec 2022 Jiajun Zhou, Chenxuan Xie, Zhenyu Wen, Xiangyu Zhao, Qi Xuan

In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems.

Data Augmentation Graph Representation Learning

A Comprehensive Survey on Trustworthy Recommender Systems

no code implementations21 Sep 2022 Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingran Su, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, Qing Li

As one of the most successful AI-powered applications, recommender systems aim to help people make appropriate decisions in an effective and efficient way, by providing personalized suggestions in many aspects of our lives, especially for various human-oriented online services such as e-commerce platforms and social media sites.

Fairness Recommendation Systems

A Privacy-Aware Graph Contrastive Learning Method in Federated Settings

no code implementations24 Jul 2022 Haoran Yang, Xiangyu Zhao, Muyang Li, Hongxu Chen, Guandong Xu

In this paper, we investigate how to implement differential privacy on graph edges and observe the performances decreasing in the experiments.

Contrastive Learning Federated Learning +2

Knowledge-enhanced Black-box Attacks for Recommendations

no code implementations21 Jul 2022 Jingfan Chen, Wenqi Fan, Guanghui Zhu, Xiangyu Zhao, Chunfeng Yuan, Qing Li, Yihua Huang

Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i. e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items.

Recommendation Systems

Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning

no code implementations1 Jul 2022 Haoran Yang, Hongxu Chen, Sixiao Zhang, Xiangguo Sun, Qian Li, Xiangyu Zhao, Guandong Xu

In this paper, we propose a novel method to utilize \textbf{C}ounterfactual mechanism to generate artificial hard negative samples for \textbf{G}raph \textbf{C}ontrastive learning, namely \textbf{CGC}, which has a different perspective compared to those sampling-based strategies.

Contrastive Learning Graph Learning +1

SC-Ques: A Sentence Completion Question Dataset for English as a Second Language Learners

1 code implementation24 Jun 2022 Qiongqiong Liu, Yaying Huang, Zitao Liu, Shuyan Huang, Jiahao Chen, Xiangyu Zhao, Guimin Lin, Yuyu Zhou, Weiqi Luo

Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options.

Sentence Completion

Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing

no code implementations23 May 2022 Xin Wang, Sheng Wang, Honglin Xiong, Kai Xuan, Zixu Zhuang, Mengjun Liu, Zhenrong Shen, Xiangyu Zhao, Lichi Zhang, Qian Wang

Magnetic resonance (MR) images collected in 2D clinical protocols typically have large inter-slice spacing, resulting in high in-plane resolution and reduced through-plane resolution.


MLP4Rec: A Pure MLP Architecture for Sequential Recommendations

no code implementations25 Apr 2022 Muyang Li, Xiangyu Zhao, Chuan Lyu, Minghao Zhao, Runze Wu, Ruocheng Guo

In addition, most existing works assume that such sequential dependencies exist solely in the item embeddings, but neglect their existence among the item features.

Recommendation Systems

AutoField: Automating Feature Selection in Deep Recommender Systems

no code implementations19 Apr 2022 Yejing Wang, Xiangyu Zhao, Tong Xu, Xian Wu

Thereby, feature selection is a critical process in developing deep learning-based recommender systems.

AutoML feature selection +1

A Comprehensive Survey on Automated Machine Learning for Recommendations

no code implementations4 Apr 2022 Bo Chen, Xiangyu Zhao, Yejing Wang, Wenqi Fan, Huifeng Guo, Ruiming Tang

Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences.

AutoML BIG-bench Machine Learning +2

Prior Attention Network for Multi-Lesion Segmentation in Medical Images

1 code implementation10 Oct 2021 Xiangyu Zhao, Peng Zhang, Fan Song, Chenbin Ma, Guangda Fan, Yangyang Sun, Youdan Feng, Guanglei Zhang

The proposed network can be regarded as a universal solution to multi-lesion segmentation in both 2D and 3D tasks.

Lesion Segmentation

Graph Trend Filtering Networks for Recommendations

1 code implementation12 Aug 2021 Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li

The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e. g., clicks, add-to-cart, purchases, etc.

Collaborative Filtering Graph Representation Learning +1

AutoLoss: Automated Loss Function Search in Recommendations

no code implementations12 Jun 2021 Xiangyu Zhao, Haochen Liu, Wenqi Fan, Hui Liu, Jiliang Tang, Chong Wang

Unlike existing algorithms, the proposed controller can adaptively generate the loss probabilities for different data examples according to their varied convergence behaviors.

Recommendation Systems

Automated Self-Supervised Learning for Graphs

1 code implementation ICLR 2022 Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, Jiliang Tang

Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks.

Node Classification Node Clustering +1

Data-Efficient Reinforcement Learning for Malaria Control

no code implementations4 May 2021 Lixin Zou, Long Xia, Linfang Hou, Xiangyu Zhao, Dawei Yin

This work introduces a practical, data-efficient policy learning method, named Variance-Bonus Monte Carlo Tree Search~(VB-MCTS), which can copy with very little data and facilitate learning from scratch in only a few trials.

Decision Making Model-based Reinforcement Learning +2

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

1 code implementation10 Feb 2021 Xiangyu Zhao, Peng Zhang, Fan Song, Guangda Fan, Yangyang Sun, Yujia Wang, Zheyuan Tian, Luqi Zhang, Guanglei Zhang

In this paper we propose a dilated dual attention U-Net (D2A U-Net) for COVID-19 lesion segmentation in CT slices based on dilated convolution and a novel dual attention mechanism to address the issues above.

Computed Tomography (CT) Lesion Segmentation +1

Towards Long-term Fairness in Recommendation

1 code implementation10 Jan 2021 Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, Yongfeng Zhang

We focus on the fairness of exposure of items in different groups, while the division of the groups is based on item popularity, which dynamically changes over time in the recommendation process.

Fairness Recommendation Systems

Watermarking Graph Neural Networks by Random Graphs

no code implementations1 Nov 2020 Xiangyu Zhao, Hanzhou Wu, Xinpeng Zhang

Many learning tasks require us to deal with graph data which contains rich relational information among elements, leading increasing graph neural network (GNN) models to be deployed in industrial products for improving the quality of service.

Model Compression

Neural Interactive Collaborative Filtering

1 code implementation4 Jul 2020 Lixin Zou, Long Xia, Yulong Gu, Xiangyu Zhao, Weidong Liu, Jimmy Xiangji Huang, Dawei Yin

Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning.

Collaborative Filtering Meta-Learning +2

Memory-efficient Embedding for Recommendations

no code implementations26 Jun 2020 Xiangyu Zhao, Haochen Liu, Hui Liu, Jiliang Tang, Weiwei Guo, Jun Shi, Sida Wang, Huiji Gao, Bo Long

Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.

AutoML Recommendation Systems

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

no code implementations17 May 2020 Wenqi Fan, Tyler Derr, Xiangyu Zhao, Yao Ma, Hui Liu, Jian-Ping Wang, Jiliang Tang, Qing Li

In this work, we present our framework CopyAttack, which is a reinforcement learning based black-box attack method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items.

Data Poisoning Recommendation Systems

Jointly Learning to Recommend and Advertise

no code implementations28 Feb 2020 Xiangyu Zhao, Xudong Zheng, Xiwang Yang, Xiaobing Liu, Jiliang Tang

Online recommendation and advertising are two major income channels for online recommendation platforms (e. g. e-commerce and news feed site).

AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations

no code implementations26 Feb 2020 Xiangyu Zhao, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, Jiliang Tang

Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e. g. user/item identifiers) and meaningfully transform them in the low-dimensional space.

AutoML Recommendation Systems

A Semi-Supervised Stable Variational Network for Promoting Replier-Consistency in Dialogue Generation

no code implementations IJCNLP 2019 Jinxin Chang, Ruifang He, Longbiao Wang, Xiangyu Zhao, Ting Yang, Ruifang Wang

However, the sampled information from latent space usually becomes useless due to the KL divergence vanishing issue, and the highly abstractive global variables easily dilute the personal features of replier, leading to a non replier-specific response.

Dialogue Generation

DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems

no code implementations9 Sep 2019 Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang, Xiwang Yang, Xiaobing Liu, Jiliang Tang, Hui Liu

However, most RL-based advertising algorithms focus on optimizing ads' revenue while ignoring the possible negative influence of ads on user experience of recommended items (products, articles and videos).

Recommendation Systems reinforcement-learning +1

Toward Simulating Environments in Reinforcement Learning Based Recommendations

no code implementations27 Jun 2019 Xiangyu Zhao, Long Xia, Lixin Zou, Dawei Yin, Jiliang Tang

Thus, it calls for a user simulator that can mimic real users' behaviors where we can pre-train and evaluate new recommendation algorithms.

Recommendation Systems reinforcement-learning +1

Whole-Chain Recommendations

no code implementations11 Feb 2019 Xiangyu Zhao, Long Xia, Linxin Zou, Hui Liu, Dawei Yin, Jiliang Tang

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems.

Multi-agent Reinforcement Learning Recommendation Systems +1

Deep reinforcement learning for search, recommendation, and online advertising: a survey

no code implementations18 Dec 2018 Xiangyu Zhao, Long Xia, Jiliang Tang, Dawei Yin

Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web.

reinforcement-learning Reinforcement Learning (RL)

Deep Reinforcement Learning for Page-wise Recommendations

no code implementations7 May 2018 Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, Jiliang Tang

In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users.

Recommendation Systems reinforcement-learning +1

Deep Reinforcement Learning for List-wise Recommendations

7 code implementations30 Dec 2017 Xiangyu Zhao, Liang Zhang, Long Xia, Zhuoye Ding, Dawei Yin, Jiliang Tang

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services.

Recommendation Systems reinforcement-learning +1

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