Search Results for author: Xiangyu Zhao

Found 124 papers, 59 papers with code

GPRec: Bi-level User Modeling for Deep Recommenders

1 code implementation28 Oct 2024 Yejing Wang, Dong Xu, Xiangyu Zhao, Zhiren Mao, Peng Xiang, Ling Yan, Yao Hu, Zijian Zhang, Xuetao Wei, Qidong Liu

GPRec explicitly categorizes users into groups in a learnable manner and aligns them with corresponding group embeddings.

Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding

no code implementations21 Oct 2024 Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

In this paper, we address these hallucination issues in the context of Medical Information Extraction (MIE) tasks by introducing ALternate Contrastive Decoding (ALCD).

Hallucination

REHRSeg: Unleashing the Power of Self-Supervised Super-Resolution for Resource-Efficient 3D MRI Segmentation

no code implementations14 Oct 2024 Zhiyun Song, Yinjie Zhao, Xiaomin Li, Manman Fei, Xiangyu Zhao, Mengjun Liu, Cunjian Chen, Chung-Hsing Yeh, Qian Wang, Guoyan Zheng, Songtao Ai, Lichi Zhang

High-resolution (HR) 3D magnetic resonance imaging (MRI) can provide detailed anatomical structural information, enabling precise segmentation of regions of interest for various medical image analysis tasks.

Knowledge Distillation Medical Image Analysis +3

PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning

no code implementations9 Oct 2024 Jiashi Gao, Ziwei Wang, Xiangyu Zhao, Xin Yao, Xuetao Wei

However, these studies primarily focus on perturbing accuracy, leaving a critical question unexplored: Can an attacker bypass the group fairness mechanisms in FL and manipulate the global model to be biased?

Fairness Federated Learning +1

LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning

no code implementations2 Sep 2024 Haoran Yang, Xiangyu Zhao, Sirui Huang, Qing Li, Guandong Xu

Graph Contrastive Learning (GCL) is a potent paradigm for self-supervised graph learning that has attracted attention across various application scenarios.

Contrastive Learning Data Augmentation +3

UniFashion: A Unified Vision-Language Model for Multimodal Fashion Retrieval and Generation

1 code implementation21 Aug 2024 Xiangyu Zhao, Yuehan Zhang, Wenlong Zhang, Xiao-Ming Wu

In this work, we present UniFashion, a unified framework that simultaneously tackles the challenges of multimodal generation and retrieval tasks within the fashion domain, integrating image generation with retrieval tasks and text generation tasks.

Image Generation Image Retrieval +5

Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework

no code implementations21 Aug 2024 Xiao Han, Chen Zhu, Xiangyu Zhao, HengShu Zhu

Visual geo-localization demands in-depth knowledge and advanced reasoning skills to associate images with precise real-world geographic locations.

geo-localization Language Modelling +2

Bidirectional Gated Mamba for Sequential Recommendation

1 code implementation21 Aug 2024 Ziwei Liu, Qidong Liu, Yejing Wang, Wanyu Wang, Pengyue Jia, Maolin Wang, Zitao Liu, Yi Chang, Xiangyu Zhao

In various domains, Sequential Recommender Systems (SRS) have become essential due to their superior capability to discern intricate user preferences.

Mamba Sequential Recommendation +1

Extracting polygonal footprints in off-nadir images with Segment Anything Model

1 code implementation16 Aug 2024 Kai Li, Jingbo Chen, Yupeng Deng, Yu Meng, Diyou Liu, Junxian Ma, Chenhao Wang, Xiangyu Zhao

Building Footprint Extraction (BFE) from off-nadir aerial images often involves roof segmentation and offset prediction to adjust roof boundaries to the building footprint.

RAG

Prompt Tuning as User Inherent Profile Inference Machine

no code implementations13 Aug 2024 Yusheng Lu, Zhaocheng Du, Xiangyang Li, Xiangyu Zhao, Weiwen Liu, Yichao Wang, Huifeng Guo, Ruiming Tang, Zhenhua Dong, Yongrui Duan

And employs expectation maximization to infer the embedded latent profile, minimizing textual noise by fixing the prompt template.

Quantization Recommendation Systems +1

Towards Robust Knowledge Tracing Models via k-Sparse Attention

1 code implementation24 Jul 2024 Shuyan Huang, Zitao Liu, Xiangyu Zhao, Weiqi Luo, Jian Weng

We show that our \textsc{sparseKT} is able to help attentional KT models get rid of irrelevant student interactions and have comparable predictive performance when compared to 11 state-of-the-art KT models on three publicly available real-world educational datasets.

Knowledge Tracing

Scalable Dynamic Embedding Size Search for Streaming Recommendation

no code implementations22 Jul 2024 Yunke Qu, Liang Qu, Tong Chen, Xiangyu Zhao, Quoc Viet Hung Nguyen, Hongzhi Yin

Although a few methods attempt to mitigate this by employing embedding size search strategies to assign different embedding dimensions in streaming recommendations, they assume that the embedding size grows with the frequency of users/items, which eventually still exceeds the predefined memory budget over time.

Recommendation Systems

Dual Test-time Training for Out-of-distribution Recommender System

no code implementations22 Jul 2024 Xihong Yang, Yiqi Wang, Jin Chen, Wenqi Fan, Xiangyu Zhao, En Zhu, Xinwang Liu, Defu Lian

To address this challenge, we propose a novel Dual Test-Time-Training framework for OOD Recommendation, termed DT3OR.

Recommendation Systems

VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models

1 code implementation16 Jul 2024 Haodong Duan, Junming Yang, Yuxuan Qiao, Xinyu Fang, Lin Chen, YuAn Liu, Amit Agarwal, Zhe Chen, Mo Li, Yubo Ma, Hailong Sun, Xiangyu Zhao, Junbo Cui, Xiaoyi Dong, Yuhang Zang, Pan Zhang, Jiaqi Wang, Dahua Lin, Kai Chen

Based on the evaluation results obtained with the toolkit, we host OpenVLM Leaderboard, a comprehensive leaderboard to track the progress of multi-modality learning research.

Auto Cherry-Picker: Learning from High-quality Generative Data Driven by Language

no code implementations28 Jun 2024 Yicheng Chen, Xiangtai Li, Yining Li, Yanhong Zeng, Jianzong Wu, Xiangyu Zhao, Kai Chen

In particular, we present a new metric, Composite Layout and Image Score (CLIS), to evaluate the generated images fairly.

MG-LLaVA: Towards Multi-Granularity Visual Instruction Tuning

1 code implementation25 Jun 2024 Xiangyu Zhao, Xiangtai Li, Haodong Duan, Haian Huang, Yining Li, Kai Chen, Hua Yang

We propose the integration of an additional high-resolution visual encoder to capture fine-grained details, which are then fused with base visual features through a Conv-Gate fusion network.

Object Object Recognition +1

Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning

1 code implementation24 Jun 2024 Xiao Han, Chen Zhu, Xiao Hu, Chuan Qin, Xiangyu Zhao, HengShu Zhu

To address this issue, we propose a novel session-based framework, BISTRO, to timely model user preference through fusion learning of semantic and behavioral information.

Clustering Recommendation Systems

MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding

1 code implementation20 Jun 2024 Xinyu Fang, Kangrui Mao, Haodong Duan, Xiangyu Zhao, Yining Li, Dahua Lin, Kai Chen

The advent of large vision-language models (LVLMs) has spurred research into their applications in multi-modal contexts, particularly in video understanding.

Video Understanding

LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation

no code implementations18 Jun 2024 Yuhao Wang, Yichao Wang, Zichuan Fu, Xiangyang Li, Xiangyu Zhao, Huifeng Guo, Ruiming Tang

As the demand for more personalized recommendation grows and a dramatic boom in commercial scenarios arises, the study on multi-scenario recommendation (MSR) has attracted much attention, which uses the data from all scenarios to simultaneously improve their recommendation performance.

Language Modelling Large Language Model +1

LLM-enhanced Reranking in Recommender Systems

no code implementations18 Jun 2024 Jingtong Gao, Bo Chen, Xiangyu Zhao, Weiwen Liu, Xiangyang Li, Yichao Wang, Zijian Zhang, Wanyu Wang, Yuyang Ye, Shanru Lin, Huifeng Guo, Ruiming Tang

Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms.

Diversity Fairness +1

GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems

1 code implementation6 Jun 2024 Sheng Zhang, Maolin Wang, Xiangyu Zhao

In the rapidly evolving field of artificial intelligence, transformer-based models have gained significant attention in the context of Sequential Recommender Systems (SRSs), demonstrating remarkable proficiency in capturing user-item interactions.

Sequential Recommendation

Large Language Models Enhanced Sequential Recommendation for Long-tail User and Item

1 code implementation31 May 2024 Qidong Liu, Xian Wu, Xiangyu Zhao, Yejing Wang, Zijian Zhang, Feng Tian, Yefeng Zheng

These challenges, termed as the long-tail user and long-tail item dilemmas, often create obstacles for traditional SRS methods.

Sequential Recommendation

M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework

1 code implementation29 Apr 2024 Zijian Zhang, Shuchang Liu, Jiaao Yu, Qingpeng Cai, Xiangyu Zhao, Chunxu Zhang, Ziru Liu, Qidong Liu, Hongwei Zhao, Lantao Hu, Peng Jiang, Kun Gai

M3oE integrates multi-domain information, maps knowledge across domains and tasks, and optimizes multiple objectives.

AutoML

ControlTraj: Controllable Trajectory Generation with Topology-Constrained Diffusion Model

no code implementations23 Apr 2024 Yuanshao Zhu, James Jianqiao Yu, Xiangyu Zhao, Qidong Liu, Yongchao Ye, Wei Chen, Zijian Zhang, Xuetao Wei, Yuxuan Liang

Generating trajectory data is among promising solutions to addressing privacy concerns, collection costs, and proprietary restrictions usually associated with human mobility analyses.

Denoising Diversity

When Emotional Stimuli meet Prompt Designing: An Auto-Prompt Graphical Paradigm

no code implementations16 Apr 2024 Chenggian Ma, Xiangyu Zhao, Chunhui Zhang, Yanzhao Qin, Wentao Zhang

With the development of Large Language Models (LLM), numerous prompts have been proposed, each with a rich set of features and their own merits.

Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention

1 code implementation4 Apr 2024 Ziru Liu, Shuchang Liu, Zijian Zhang, Qingpeng Cai, Xiangyu Zhao, Kesen Zhao, Lantao Hu, Peng Jiang, Kun Gai

In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards.

Contrastive Learning Multi-Task Learning +2

ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems

2 code implementations19 Mar 2024 Pengyue Jia, Yejing Wang, Zhaocheng Du, Xiangyu Zhao, Yichao Wang, Bo Chen, Wanyu Wang, Huifeng Guo, Ruiming Tang

Secondly, the existing literature's lack of detailed analysis on selection attributes, based on large-scale datasets and a thorough comparison among selection techniques and DRS backbones, restricts the generalizability of findings and impedes deployment on DRS.

Benchmarking feature selection +1

The 2nd Workshop on Recommendation with Generative Models

no code implementations7 Mar 2024 Wenjie Wang, Yang Zhang, Xinyu Lin, Fuli Feng, Weiwen Liu, Yong liu, Xiangyu Zhao, Wayne Xin Zhao, Yang song, Xiangnan He

The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations.

Recommendation Systems

Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models

1 code implementation4 Mar 2024 Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs) by making predictions for missing links.

Link Prediction Relation +1

Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models

1 code implementation28 Feb 2024 Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Wanyu Wang, Yuyang Ye, Xiangyu Zhao, Enhong Chen, Yefeng Zheng

To evaluate the editing impact on the behaviours of LLMs, we propose two model editing studies for medical domain: (1) editing factual knowledge for medical specialization and (2) editing the explanatory ability for complex knowledge.

Benchmarking Hallucination +1

BiVRec: Bidirectional View-based Multimodal Sequential Recommendation

no code implementations27 Feb 2024 Jiaxi Hu, Jingtong Gao, Xiangyu Zhao, Yuehong Hu, Yuxuan Liang, Yiqi Wang, Ming He, Zitao Liu, Hongzhi Yin

The integration of multimodal information into sequential recommender systems has attracted significant attention in recent research.

Semantic Similarity Semantic Textual Similarity +1

Enhancing Node Representations for Real-World Complex Networks with Topological Augmentation

1 code implementation20 Feb 2024 Xiangyu Zhao, Zehui Li, Mingzhu Shen, Guy-Bart Stan, Pietro Liò, Yiren Zhao

In this paper, we present Topological Augmentation (TopoAug), a novel graph augmentation method that builds a combinatorial complex from the original graph by constructing virtual hyperedges directly from the raw data.

Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation

no code implementations16 Feb 2024 Xinjian Zhao, Liang Zhang, Yang Liu, Ruocheng Guo, Xiangyu Zhao

To address this challenge, we propose an innovative framework: Adversarial Curriculum Graph Contrastive Learning (ACGCL), which capitalizes on the merits of pair-wise augmentation to engender graph-level positive and negative samples with controllable similarity, alongside subgraph contrastive learning to discern effective graph patterns therein.

Contrastive Learning Graph Representation Learning

Large Language Model Distilling Medication Recommendation Model

1 code implementation5 Feb 2024 Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Zijian Zhang, Feng Tian, Yefeng Zheng

In this paper, we introduce a novel approach called Large Language Model Distilling Medication Recommendation (LEADER).

Knowledge Distillation Language Modelling +2

Cumulative Distribution Function based General Temporal Point Processes

no code implementations1 Feb 2024 Maolin Wang, Yu Pan, Zenglin Xu, Ruocheng Guo, Xiangyu Zhao, Wanyu Wang, Yiqi Wang, Zitao Liu, Langming Liu

Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction, and empirical validation of CuFun's effectiveness through extensive experimentation on synthetic and real-world datasets.

Information Retrieval Point Processes +1

An Open and Comprehensive Pipeline for Unified Object Grounding and Detection

2 code implementations4 Jan 2024 Xiangyu Zhao, Yicheng Chen, Shilin Xu, Xiangtai Li, Xinjiang Wang, Yining Li, Haian Huang

Grounding-DINO is a state-of-the-art open-set detection model that tackles multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC).

Described Object Detection Phrase Grounding +2

Large Language Models for Generative Information Extraction: A Survey

1 code implementation29 Dec 2023 Derong Xu, Wei Chen, Wenjun Peng, Chao Zhang, Tong Xu, Xiangyu Zhao, Xian Wu, Yefeng Zheng, Yang Wang, Enhong Chen

Information extraction (IE) aims to extract structural knowledge from plain natural language texts.

Survey

Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM

no code implementations24 Dec 2023 Xiaopeng Li, Lixin Su, Pengyue Jia, Xiangyu Zhao, Suqi Cheng, Junfeng Wang, Dawei Yin

To be specific, we use Chain of Thought (CoT) technology to utilize Large Language Models (LLMs) as agents to emulate various demographic profiles, then use them for efficient query rewriting, and we innovate a robust Multi-gate Mixture of Experts (MMoE) architecture coupled with a hybrid loss function, collectively strengthening the ranking models' robustness.

A Unified Framework for Multi-Domain CTR Prediction via Large Language Models

1 code implementation17 Dec 2023 Zichuan Fu, Xiangyang Li, Chuhan Wu, Yichao Wang, Kuicai Dong, Xiangyu Zhao, Mengchen Zhao, Huifeng Guo, Ruiming Tang

Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them.

Click-Through Rate Prediction Language Modelling +2

Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroup

no code implementations10 Dec 2023 Maolin Wang, Yao Zhao, Jiajia Liu, Jingdong Chen, Chenyi Zhuang, Jinjie Gu, Ruocheng Guo, Xiangyu Zhao

In our research, we constructed a dataset, the Multimodal Advertisement Audition Dataset (MAAD), from real-world scenarios within Alipay, and conducted experiments to validate the reliability of our proposed strategy.

Model Compression

E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential Recommendation

2 code implementations5 Dec 2023 Xinhang Li, Chong Chen, Xiangyu Zhao, Yong Zhang, Chunxiao Xing

Furthermore, practical ID-based recommendation strategies, reliant on a huge number of unique identities (IDs) to represent users and items, have gained prominence in real-world recommender systems due to their effectiveness and efficiency.

Sequential Recommendation Text Generation

Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization

no code implementations17 Nov 2023 Maolin Wang, Dun Zeng, Zenglin Xu, Ruocheng Guo, Xiangyu Zhao

To address these issues, we propose a novel method, i. e., Federated Latent Embedding Sharing Tensor factorization (FLEST), which is a novel approach using federated tensor factorization for KG completion.

MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis

1 code implementation14 Nov 2023 Yitao Zhu, Zhenrong Shen, Zihao Zhao, Sheng Wang, Xin Wang, Xiangyu Zhao, Dinggang Shen, Qian Wang

By fixing the weight of ViT models and only adding small low-rank plug-ins, we achieve competitive results on various diagnosis tasks across different imaging modalities using only a few trainable parameters.

Embedding in Recommender Systems: A Survey

1 code implementation28 Oct 2023 Xiangyu Zhao, Maolin Wang, Xinjian Zhao, Jiansheng Li, Shucheng Zhou, Dawei Yin, Qing Li, Jiliang Tang, Ruocheng Guo

This survey covers embedding methods like collaborative filtering, self-supervised learning, and graph-based techniques.

AutoML Collaborative Filtering +4

Enhancing Traffic Prediction with Learnable Filter Module

no code implementations24 Oct 2023 Yuanshao Zhu, Yongchao Ye, Xiangyu Zhao, James J. Q. Yu

Our approach focuses on enhancing the quality of the input data for traffic prediction models, which is a critical yet often overlooked aspect in the field.

Traffic Prediction

When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications

3 code implementations21 Oct 2023 Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu, Derong Xu, Feng Tian, Yefeng Zheng

To address these two problems, we propose a novel parameter efficient fine-tuning framework for multi-task medical applications, dubbed as MOELoRA.

Multi-Task Learning parameter-efficient fine-tuning

EasyGen: Easing Multimodal Generation with BiDiffuser and LLMs

1 code implementation13 Oct 2023 Xiangyu Zhao, Bo Liu, Qijiong Liu, Guangyuan Shi, Xiao-Ming Wu

We present EasyGen, an efficient model designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLMs), Unlike existing multimodal models that predominately depend on encoders like CLIP or ImageBind and need ample amounts of training data to bridge modalities, EasyGen leverages BiDiffuser, a bidirectional conditional diffusion model, to foster more efficient modality interactions.

multimodal generation Text Generation +1

MLPST: MLP is All You Need for Spatio-Temporal Prediction

no code implementations23 Sep 2023 Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S. Joe Qin, Hongwei Zhao

To accomplish the above goals, we propose an intuitive and novel framework, MLPST, a pure multi-layer perceptron architecture for traffic prediction.

Traffic Prediction

KuaiSim: A Comprehensive Simulator for Recommender Systems

1 code implementation NeurIPS 2023 Kesen Zhao, Shuchang Liu, Qingpeng Cai, Xiangyu Zhao, Ziru Liu, Dong Zheng, Peng Jiang, Kun Gai

For each task, KuaiSim also provides evaluation protocols and baseline recommendation algorithms that further serve as benchmarks for future research.

Reinforcement Learning (RL) Sequential Recommendation

Diffusion Augmentation for Sequential Recommendation

2 code implementations22 Sep 2023 Qidong Liu, Fan Yan, Xiangyu Zhao, Zhaocheng Du, Huifeng Guo, Ruiming Tang, Feng Tian

However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems.

Data Augmentation Sequential Recommendation

Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting

1 code implementation20 Sep 2023 Qian Ma, Zijian Zhang, Xiangyu Zhao, Haoliang Li, Hongwei Zhao, Yiqi Wang, Zitao Liu, Wanyu Wang

Then, we generate representative and common spatio-temporal patterns as global nodes to reflect a global dependency between sensors and provide auxiliary information for spatio-temporal dependency learning.

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

1 code implementation18 Sep 2023 Zijian Zhang, Xiangyu Zhao, Qidong Liu, Chunxu Zhang, Qian Ma, Wanyu Wang, Hongwei Zhao, Yiqi Wang, Zitao Liu

We devise a spatio-temporal transformer and a parameter-sharing training scheme to address the common knowledge among different spatio-temporal attributes.

Attribute

HAMUR: Hyper Adapter for Multi-Domain Recommendation

2 code implementations12 Sep 2023 Xiaopeng Li, Fan Yan, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, Ruiming Tang

Secondly, due to the distribution differences among domains, the utilization of static parameters in existing methods limits their flexibility to adapt to diverse domains.

Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation

no code implementations5 Sep 2023 Jingtong Gao, Bo Chen, Menghui Zhu, Xiangyu Zhao, Xiaopeng Li, Yuhao Wang, Yichao Wang, Huifeng Guo, Ruiming Tang

To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively and conducts explicit and implicit scenario modeling jointly.

Click-Through Rate Prediction

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

1 code implementation2 Sep 2023 Xiangyu Zhao, Sheng Wang, Zhiyun Song, Zhenrong Shen, Linlin Yao, Haolei Yuan, Qian Wang, Lichi Zhang

To address these issues, we propose a novel one-shot medical image segmentation method with adversarial training and label error rectification (AdLER), with the aim of improving the diversity of generated data and correcting label errors to enhance segmentation performance.

Anatomy Data Augmentation +5

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

no code implementations23 Aug 2023 Xiandong Zou, Xiangyu Zhao, Pietro Liò, Yiren Zhao

Moreover, we show that GCPN and GraphAF with advanced GNNs can achieve state-of-the-art results across 17 other non-GNN-based graph generative approaches, such as variational autoencoders and Bayesian optimisation models, on the proposed molecular generative objectives (DRD2, Median1, Median2), which are impor- tant metrics for de-novo molecular design.

Bayesian Optimisation Graph Generation +2

AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation

no code implementations14 Aug 2023 Ziru Liu, Kecheng Chen, Fengyi Song, Bo Chen, Xiangyu Zhao, Huifeng Guo, Ruiming Tang

In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly.

Recommendation Systems

OpenSiteRec: An Open Dataset for Site Recommendation

no code implementations3 Jul 2023 Xinhang Li, Xiangyu Zhao, Yejing Wang, Yu Liu, Yong Li, Cheng Long, Yong Zhang, Chunxiao Xing

As a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic data-driven way, is beneficial and crucial for brand development in modern business.

Benchmarking Information Retrieval +1

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

no code implementations8 Jun 2023 Zehui Li, Xiangyu Zhao, Mingzhu Shen, Guy-Bart Stan, Pietro Liò, Yiren Zhao

Additionally, though many Graph Neural Networks (GNNs) have been proposed for representation learning on higher-order graphs, they are usually only evaluated on simple graph datasets.

Graph Learning Representation Learning

Tensorized Hypergraph Neural Networks

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

THNN is a faithful hypergraph modeling framework through high-order outer product feature message passing and is a natural tensor extension of the adjacency-matrix-based graph neural networks.

DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model

1 code implementation NeurIPS 2023 Yuanshao Zhu, Yongchao Ye, Shiyao Zhang, Xiangyu Zhao, James J. Q. Yu

In this work, we propose a spatial-temporal diffusion probabilistic model for trajectory generation (DiffTraj).

Denoising

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

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 counterfactual +1

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

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

2 code implementations8 Feb 2023 Qidong Liu, Jiaxi Hu, Yutian Xiao, Xiangyu Zhao, Jingtong Gao, Wanyu Wang, Qing Li, Jiliang Tang

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

Attribute Model Optimization +2

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 +2

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 +3

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

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 +4

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 +3

Data Augmentation on Graphs: A Technical Survey

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

To advance research in this emerging direction, this survey provides a comprehensive review and summary of existing graph data augmentation (GDAug) techniques.

Data Augmentation Graph Representation Learning +1

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 +1

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.

Attribute 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 counterfactual +2

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 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.

Computational Efficiency Super-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

1 code implementation19 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 +3

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.

Clustering Node Classification +2

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 +3

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) Decoder +3

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.

Graph Neural Network 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

1 code implementation17 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 Deep Learning +1

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).

Reinforcement Learning

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 +2

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.

Generative Adversarial Network Recommendation Systems +3

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 +2

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 +1

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 +2

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 +2

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