Search Results for author: Xing Xie

Found 136 papers, 72 papers with code

Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search

no code implementations Findings (EMNLP) 2021 Shuxian Bi, Chaozhuo Li, Xiao Han, Zheng Liu, Xing Xie, Haizhen Huang, Zengxuan Wen

As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value.


Matching-oriented Embedding Quantization For Ad-hoc Retrieval

1 code implementation EMNLP 2021 Shitao Xiao, Zheng Liu, Yingxia Shao, Defu Lian, Xing Xie

In this work, we propose the Matching-oriented Product Quantization (MoPQ), where a novel objective Multinoulli Contrastive Loss (MCL) is formulated.

Quantization Retrieval

Continual Learning on Dynamic Graphs via Parameter Isolation

1 code implementation23 May 2023 Peiyan Zhang, Yuchen Yan, Chaozhuo Li, Senzhang Wang, Xing Xie, Guojie Song, Sunghun Kim

Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs.

Continual Learning Graph Learning

Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations

no code implementations22 May 2023 Hao Chen, Ankit Shah, Jindong Wang, Ran Tao, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj

In this paper, we introduce the imprecise label learning (ILL) framework, a unified approach to handle various imprecise label configurations, which are commonplace challenges in machine learning tasks.

Learning with noisy labels Partial Label Learning

Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark

1 code implementation17 May 2023 Wenjun Peng, Jingwei Yi, Fangzhao Wu, Shangxi Wu, Bin Zhu, Lingjuan Lyu, Binxing Jiao, Tong Xu, Guangzhong Sun, Xing Xie

Companies have begun to offer Embedding as a Service (EaaS) based on these LLMs, which can benefit various natural language processing (NLP) tasks for customers.

Model extraction

Towards Explainable Collaborative Filtering with Taste Clusters Learning

1 code implementation27 Apr 2023 Yuntao Du, Jianxun Lian, Jing Yao, Xiting Wang, Mingqi Wu, Lu Chen, Yunjun Gao, Xing Xie

In recent decades, there have been significant advancements in latent embedding-based CF methods for improved accuracy, such as matrix factorization, neural collaborative filtering, and LightGCN.

Collaborative Filtering Decision Making +3

Exploring Vision-Language Models for Imbalanced Learning

1 code implementation4 Apr 2023 Yidong Wang, Zhuohao Yu, Jindong Wang, Qiang Heng, Hao Chen, Wei Ye, Rui Xie, Xing Xie, Shikun Zhang

However, their performance on imbalanced dataset is relatively poor, where the distribution of classes in the training dataset is skewed, leading to poor performance in predicting minority classes.

Zero-Shot Learning

IRGen: Generative Modeling for Image Retrieval

no code implementations17 Mar 2023 Yidan Zhang, Ting Zhang, Dong Chen, Yujing Wang, Qi Chen, Xing Xie, Hao Sun, Weiwei Deng, Qi Zhang, Fan Yang, Mao Yang, Qingmin Liao, Baining Guo

While generative modeling has been ubiquitous in natural language processing and computer vision, its application to image retrieval remains unexplored.

Image Retrieval Retrieval

Distillation from Heterogeneous Models for Top-K Recommendation

1 code implementation2 Mar 2023 SeongKu Kang, Wonbin Kweon, Dongha Lee, Jianxun Lian, Xing Xie, Hwanjo Yu

Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), to reduce the huge inference costs while retaining high accuracy.

Knowledge Distillation Recommendation Systems +1

FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning

1 code implementation27 Feb 2023 Wang Lu, Xixu Hu, Jindong Wang, Xing Xie

Concretely, we design an attention-based adapter for the large model, CLIP, and the rest operations merely depend on adapters.

Federated Learning Privacy Preserving

On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective

1 code implementation22 Feb 2023 Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, Binxin Jiao, Yue Zhang, Xing Xie

In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective.

Adversarial Robustness Chatbot +1

SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning

3 code implementations26 Jan 2023 Hao Chen, Ran Tao, Yue Fan, Yidong Wang, Jindong Wang, Bernt Schiele, Xing Xie, Bhiksha Raj, Marios Savvides

The critical challenge of Semi-Supervised Learning (SSL) is how to effectively leverage the limited labeled data and massive unlabeled data to improve the model's generalization performance.

imbalanced classification

DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation

no code implementations16 Dec 2022 Yuxi Feng, Xiaoyuan Yi, Xiting Wang, Laks V. S. Lakshmanan, Xing Xie

Augmented by only self-generated pseudo text, generation models over-emphasize exploitation of the previously learned space, suffering from a constrained generalization boundary.

Text Generation

CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection

1 code implementation30 Nov 2022 Jing Yao, Zheng Liu, Junhan Yang, Zhicheng Dou, Xing Xie, Ji-Rong Wen

In the first stage, a lightweight CNN-based ad-hod neighbor selector is deployed to filter useful neighbors for the matching task with a small computation cost.

Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes

no code implementations24 Nov 2022 Yiqiao Jin, Xiting Wang, Yaru Hao, Yizhou Sun, Xing Xie

In this paper, we move towards combining large parametric models with non-parametric prototypical networks.

An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning

no code implementations20 Nov 2022 Hao Chen, Yue Fan, Yidong Wang, Jindong Wang, Bernt Schiele, Xing Xie, Marios Savvides, Bhiksha Raj

While standard SSL assumes uniform data distribution, we consider a more realistic and challenging setting called imbalanced SSL, where imbalanced class distributions occur in both labeled and unlabeled data.

Pseudo Label

GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective

1 code implementation15 Nov 2022 Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, Yue Zhang

Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase.

Natural Language Understanding Out-of-Distribution Generalization

Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention

1 code implementation14 Nov 2022 Wenhao Li, Xiaoyuan Yi, Jinyi Hu, Maosong Sun, Xing Xie

In this work, we dig into the intrinsic mechanism of this problem and found that sparser attention values in Transformer could improve diversity.

Text Generation

Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization

no code implementations10 Nov 2022 Yueqi Xie, Weizhong Zhang, Renjie Pi, Fangzhao Wu, Qifeng Chen, Xing Xie, Sunghun Kim

Since at each round, the number of tunable parameters optimized on the server side equals the number of participating clients (thus independent of the model size), we are able to train a global model with massive parameters using only a small amount of proxy data (e. g., around one hundred samples).

Federated Learning

FIXED: Frustratingly Easy Domain Generalization with Mixup

1 code implementation7 Nov 2022 Wang Lu, Jindong Wang, Han Yu, Lei Huang, Xiang Zhang, Yiqiang Chen, Xing Xie

Firstly, Mixup cannot effectively identify the domain and class information that can be used for learning invariant representations.

Domain Generalization Image Classification +1

Learning on Large-scale Text-attributed Graphs via Variational Inference

2 code implementations26 Oct 2022 Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, Jian Tang

In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM.

Variational Inference

Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation

no code implementations22 Oct 2022 Jinyi Hu, Xiaoyuan Yi, Wenhao Li, Maosong Sun, Xing Xie

We demonstrate that TRACE could enhance the entanglement of each segment and preceding latent variables and deduce a non-zero lower bound of the KL term, providing a theoretical guarantee of generation diversity.

Text Generation

Test-Time Training for Graph Neural Networks

no code implementations17 Oct 2022 Yiqi Wang, Chaozhuo Li, Wei Jin, Rui Li, Jianan Zhao, Jiliang Tang, Xing Xie

To bridge such gap, in this work we introduce the first test-time training framework for GNNs to enhance the model generalization capacity for the graph classification task.

Graph Classification Self-Supervised Learning

Effective and Efficient Query-aware Snippet Extraction for Web Search

1 code implementation17 Oct 2022 Jingwei Yi, Fangzhao Wu, Chuhan Wu, Xiaolong Huang, Binxing Jiao, Guangzhong Sun, Xing Xie

In this paper, we propose an effective query-aware webpage snippet extraction method named DeepQSE, aiming to select a few sentences which can best summarize the webpage content in the context of input query.

Self-explaining deep models with logic rule reasoning

1 code implementation13 Oct 2022 Seungeon Lee, Xiting Wang, Sungwon Han, Xiaoyuan Yi, Xing Xie, Meeyoung Cha

We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision.

Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution

no code implementations1 Sep 2022 Wang Lu, Jindong Wang, Yidong Wang, Kan Ren, Yiqiang Chen, Xing Xie

For optimization, we utilize an adapted Mixup to generate an out-of-distribution dataset that can guide the preference direction and optimize with Pareto optimization.

Domain Generalization Model Optimization +1

Domain-Specific Risk Minimization for Out-of-Distribution Generalization

1 code implementation18 Aug 2022 Yi-Fan Zhang, Jindong Wang, Jian Liang, Zhang Zhang, Baosheng Yu, Liang Wang, DaCheng Tao, Xing Xie

Our bound motivates two strategies to reduce the gap: the first one is ensembling multiple classifiers to enrich the hypothesis space, then we propose effective gap estimation methods for guiding the selection of a better hypothesis for the target.

Domain Generalization Out-of-Distribution Generalization

Equivariant Disentangled Transformation for Domain Generalization under Combination Shift

no code implementations3 Aug 2022 Yivan Zhang, Jindong Wang, Xing Xie, Masashi Sugiyama

To formally analyze this issue, we provide a unique algebraic formulation of the combination shift problem based on the concepts of homomorphism, equivariance, and a refined definition of disentanglement.

Disentanglement Domain Generalization

Geometric Interaction Augmented Graph Collaborative Filtering

no code implementations2 Aug 2022 Yiding Zhang, Chaozhuo Li, Senzhang Wang, Jianxun Lian, Xing Xie

Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly research interests.

Collaborative Filtering

Domain-invariant Feature Exploration for Domain Generalization

1 code implementation25 Jul 2022 Wang Lu, Jindong Wang, Haoliang Li, Yiqiang Chen, Xing Xie

Internal invariance means that the features can be learned with a single domain and the features capture intrinsic semantics of data, i. e., the property within a domain, which is agnostic to other domains.

Domain Generalization Knowledge Distillation +1

Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network

1 code implementation26 Jun 2022 Peiyan Zhang, Jiayan Guo, Chaozhuo Li, Yueqi Xie, Jaeboum Kim, Yan Zhang, Xing Xie, Haohan Wang, Sunghun Kim

Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process.

Session-Based Recommendations

MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare

2 code implementations17 Jun 2022 Yiqiang Chen, Wang Lu, Xin Qin, Jindong Wang, Xing Xie

Federated learning has attracted increasing attention to building models without accessing the raw user data, especially in healthcare.

Federated Learning Knowledge Distillation

FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning

1 code implementation7 Jun 2022 Tao Qi, Fangzhao Wu, Chuhan Wu, Lingjuan Lyu, Tong Xu, Zhongliang Yang, Yongfeng Huang, Xing Xie

In order to learn a fair unified representation, we send it to each platform storing fairness-sensitive features and apply adversarial learning to remove bias from the unified representation inherited from the biased data.

Fairness Federated Learning +1

A Neural Corpus Indexer for Document Retrieval

1 code implementation6 Jun 2022 Yujing Wang, Yingyan Hou, Haonan Wang, Ziming Miao, Shibin Wu, Hao Sun, Qi Chen, Yuqing Xia, Chengmin Chi, Guoshuai Zhao, Zheng Liu, Xing Xie, Hao Allen Sun, Weiwei Deng, Qi Zhang, Mao Yang

To this end, we propose Neural Corpus Indexer (NCI), a sequence-to-sequence network that generates relevant document identifiers directly for a designated query.

Retrieval TriviaQA

Negative Sampling for Contrastive Representation Learning: A Review

no code implementations1 Jun 2022 Lanling Xu, Jianxun Lian, Wayne Xin Zhao, Ming Gong, Linjun Shou, Daxin Jiang, Xing Xie, Ji-Rong Wen

The learn-to-compare paradigm of contrastive representation learning (CRL), which compares positive samples with negative ones for representation learning, has achieved great success in a wide range of domains, including natural language processing, computer vision, information retrieval and graph learning.

Graph Learning Information Retrieval +2

Robust Quantity-Aware Aggregation for Federated Learning

no code implementations22 May 2022 Jingwei Yi, Fangzhao Wu, Huishuai Zhang, Bin Zhu, Tao Qi, Guangzhong Sun, Xing Xie

Federated learning (FL) enables multiple clients to collaboratively train models without sharing their local data, and becomes an important privacy-preserving machine learning framework.

Federated Learning Privacy Preserving

FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

3 code implementations15 May 2022 Yidong Wang, Hao Chen, Qiang Heng, Wenxin Hou, Yue Fan, Zhen Wu, Jindong Wang, Marios Savvides, Takahiro Shinozaki, Bhiksha Raj, Bernt Schiele, Xing Xie

Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.

Fairness Semi-Supervised Image Classification

FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation

no code implementations21 Apr 2022 Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie

In this paper, we propose a federated contrastive learning method named FedCL for privacy-preserving recommendation, which can exploit high-quality negative samples for effective model training with privacy well protected.

Contrastive Learning Privacy Preserving

ProFairRec: Provider Fairness-aware News Recommendation

1 code implementation10 Apr 2022 Tao Qi, Fangzhao Wu, Chuhan Wu, Peijie Sun, Le Wu, Xiting Wang, Yongfeng Huang, Xing Xie

To learn provider-fair representations from biased data, we employ provider-biased representations to inherit provider bias from data.

Fairness News Recommendation

A Mutually Reinforced Framework for Pretrained Sentence Embeddings

no code implementations28 Feb 2022 Junhan Yang, Zheng Liu, Shitao Xiao, Jianxun Lian, Lijun Wu, Defu Lian, Guangzhong Sun, Xing Xie

Instead of relying on annotation heuristics defined by humans, it leverages the sentence representation model itself and realizes the following iterative self-supervision process: on one hand, the improvement of sentence representation may contribute to the quality of data annotation; on the other hand, more effective data annotation helps to generate high-quality positive samples, which will further improve the current sentence representation model.

Contrastive Learning Sentence Embeddings

NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better

no code implementations ACL 2022 Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie

In this paper, we propose a very simple yet effective method named NoisyTune to help better finetune PLMs on downstream tasks by adding some noise to the parameters of PLMs before fine-tuning.

No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices

no code implementations16 Feb 2022 Ruixuan Liu, Fangzhao Wu, Chuhan Wu, Yanlin Wang, Lingjuan Lyu, Hong Chen, Xing Xie

In this way, all the clients can participate in the model learning in FL, and the final model can be big and powerful enough.

Federated Learning Knowledge Distillation +1

HousE: Knowledge Graph Embedding with Householder Parameterization

1 code implementation16 Feb 2022 Rui Li, Jianan Zhao, Chaozhuo Li, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang

The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties.

Knowledge Graph Embedding Relation Mapping

UA-FedRec: Untargeted Attack on Federated News Recommendation

1 code implementation14 Feb 2022 Jingwei Yi, Fangzhao Wu, Bin Zhu, Yang Yu, Chao Zhang, Guangzhong Sun, Xing Xie

Our study reveals a critical security issue in existing federated news recommendation systems and calls for research efforts to address the issue.

Federated Learning News Recommendation +2

Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction

no code implementations10 Feb 2022 Chuhan Wu, Fangzhao Wu, Tao Qi, Yanlin Wang, Yuqing Yang, Yongfeng Huang, Xing Xie

To solve the game, we propose a platform negotiation method that simulates the bargaining among platforms and locally optimizes their policies via gradient descent.

Federated Learning

FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling

no code implementations10 Feb 2022 Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie

However, existing general FL poisoning methods for degrading model performance are either ineffective or not concealed in poisoning federated recommender systems.

Federated Learning Recommendation Systems

Reinforcement Routing on Proximity Graph for Efficient Recommendation

no code implementations23 Jan 2022 Chao Feng, Defu Lian, Xiting Wang, Zheng Liu, Xing Xie, Enhong Chen

Instead of searching the nearest neighbor for the query, we search the item with maximum inner product with query on the proximity graph.

Imitation Learning Recommendation Systems

Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval

2 code implementations14 Jan 2022 Shitao Xiao, Zheng Liu, Weihao Han, Jianjin Zhang, Yingxia Shao, Defu Lian, Chaozhuo Li, Hao Sun, Denvy Deng, Liangjie Zhang, Qi Zhang, Xing Xie

In this work, we tackle this problem with Bi-Granular Document Representation, where the lightweight sparse embeddings are indexed and standby in memory for coarse-grained candidate search, and the heavyweight dense embeddings are hosted in disk for fine-grained post verification.

Quantization Retrieval

Gophormer: Ego-Graph Transformer for Node Classification

no code implementations25 Oct 2021 Jianan Zhao, Chaozhuo Li, Qianlong Wen, Yiqi Wang, Yuming Liu, Hao Sun, Xing Xie, Yanfang Ye

Existing graph transformer models typically adopt fully-connected attention mechanism on the whole input graph and thus suffer from severe scalability issues and are intractable to train in data insufficient cases.

Classification Data Augmentation +3

Towards Fine-Grained Reasoning for Fake News Detection

1 code implementation13 Sep 2021 Yiqiao Jin, Xiting Wang, Ruichao Yang, Yizhou Sun, Wei Wang, Hao Liao, Xing Xie

The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues.

Fake News Detection

Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation

1 code implementation EMNLP 2021 Jingwei Yi, Fangzhao Wu, Chuhan Wu, Ruixuan Liu, Guangzhong Sun, Xing Xie

However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients.

Federated Learning News Recommendation +1

Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving

no code implementations Findings (EMNLP) 2021 Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie

In this paper, we propose a unified news recommendation framework, which can utilize user data locally stored in user clients to train models and serve users in a privacy-preserving way.

News Generation News Recommendation +2

UserBERT: Contrastive User Model Pre-training

no code implementations3 Sep 2021 Chuhan Wu, Fangzhao Wu, Yang Yu, Tao Qi, Yongfeng Huang, Xing Xie

Two self-supervision tasks are incorporated in UserBERT for user model pre-training on unlabeled user behavior data to empower user modeling.

FedKD: Communication Efficient Federated Learning via Knowledge Distillation

no code implementations30 Aug 2021 Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Yongfeng Huang, Xing Xie

Instead of directly communicating the large models between clients and server, we propose an adaptive mutual distillation framework to reciprocally learn a student and a teacher model on each client, where only the student model is shared by different clients and updated collaboratively to reduce the communication cost.

Federated Learning Knowledge Distillation

Fastformer: Additive Attention Can Be All You Need

9 code implementations20 Aug 2021 Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang, Xing Xie

In this way, Fastformer can achieve effective context modeling with linear complexity.

 Ranked #1 on News Recommendation on MIND (using extra training data)

News Recommendation Text Classification +1

Smart Bird: Learnable Sparse Attention for Efficient and Effective Transformer

no code implementations20 Aug 2021 Chuhan Wu, Fangzhao Wu, Tao Qi, Binxing Jiao, Daxin Jiang, Yongfeng Huang, Xing Xie

We then sample token pairs based on their probability scores derived from the sketched attention matrix to generate different sparse attention index matrices for different attention heads.

PENS: A Dataset and Generic Framework for Personalized News Headline Generation

no code implementations ACL 2021 Xiang Ao, Xiting Wang, Ling Luo, Ying Qiao, Qing He, Xing Xie

To build up a benchmark for this problem, we publicize a large-scale dataset named PENS (PErsonalized News headlineS).

Headline generation

Personalized News Recommendation: Methods and Challenges

no code implementations16 Jun 2021 Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie

Instead of following the conventional taxonomy of news recommendation methods, in this paper we propose a novel perspective to understand personalized news recommendation based on its core problems and the associated techniques and challenges.

News Recommendation Recommendation Systems

HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation

no code implementations ACL 2021 Tao Qi, Fangzhao Wu, Chuhan Wu, Peiru Yang, Yang Yu, Xing Xie, Yongfeng Huang

Instead of a single user embedding, in our method each user is represented in a hierarchical interest tree to better capture their diverse and multi-grained interest in news.

News Recommendation

GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph

no code implementations NeurIPS 2021 Junhan Yang, Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, Xing Xie

The representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information.

Language Modelling Recommendation Systems +1

AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search

1 code implementation25 Apr 2021 Chaozhuo Li, Bochen Pang, Yuming Liu, Hao Sun, Zheng Liu, Xing Xie, Tianqi Yang, Yanling Cui, Liangjie Zhang, Qi Zhang

Our motivation lies in incorporating the tremendous amount of unsupervised user behavior data from the historical search logs as the complementary graph to facilitate relevance modeling.


Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks

no code implementations22 Apr 2021 Junhan Yang, Zheng Liu, Bowen Jin, Jianxun Lian, Defu Lian, Akshay Soni, Eun Yong Kang, Yajun Wang, Guangzhong Sun, Xing Xie

For the sake of efficient recommendation, conventional methods would generate user and advertisement embeddings independently with a siamese transformer encoder, such that approximate nearest neighbour search (ANN) can be leveraged.


Matching-oriented Product Quantization For Ad-hoc Retrieval

2 code implementations16 Apr 2021 Shitao Xiao, Zheng Liu, Yingxia Shao, Defu Lian, Xing Xie

In this work, we propose the Matching-oriented Product Quantization (MoPQ), where a novel objective Multinoulli Contrastive Loss (MCL) is formulated.

Quantization Retrieval

DebiasedRec: Bias-aware User Modeling and Click Prediction for Personalized News Recommendation

no code implementations15 Apr 2021 Jingwei Yi, Fangzhao Wu, Chuhan Wu, Qifei Li, Guangzhong Sun, Xing Xie

The core of our method includes a bias representation module, a bias-aware user modeling module, and a bias-aware click prediction module.

News Recommendation

Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations

1 code implementation18 Feb 2021 Jianxun Lian, Iyad Batal, Zheng Liu, Akshay Soni, Eun Yong Kang, Yajun Wang, Xing Xie

User states in different channels are updated by an \emph{erase-and-add} paradigm with interest- and instance-level attention.

Recommendation Systems

Training Large-Scale News Recommenders with Pretrained Language Models in the Loop

no code implementations18 Feb 2021 Shitao Xiao, Zheng Liu, Yingxia Shao, Tao Di, Xing Xie

Secondly, it improves the data efficiency of the training workflow, where non-informative data can be eliminated from encoding.

News Recommendation Recommendation Systems

FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation

no code implementations9 Feb 2021 Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, Xing Xie

To incorporate high-order user-item interactions, we propose a user-item graph expansion method that can find neighboring users with co-interacted items and exchange their embeddings for expanding the local user-item graphs in a privacy-preserving way.

Privacy Preserving

Neural News Recommendation with Negative Feedback

no code implementations12 Jan 2021 Chuhan Wu, Fangzhao Wu, Yongfeng Huang, Xing Xie

The dwell time of news reading is an important clue for user interest modeling, since short reading dwell time usually indicates low and even negative interest.

News Recommendation

Fake News Detection through Graph Comment Advanced Learning

no code implementations3 Nov 2020 Hao Liao, Qixin Liu, Kai Shu, Xing Xie

Yet, the popularity of social media also provides opportunities to better detect fake news.

Fake News Detection Representation Learning Social and Information Networks

Sampling-Decomposable Generative Adversarial Recommender

no code implementations NeurIPS 2020 Binbin Jin, Defu Lian, Zheng Liu, Qi Liu, Jianhui Ma, Xing Xie, Enhong Chen

The GAN-style recommenders (i. e., IRGAN) addresses the challenge by learning a generator and a discriminator adversarially, such that the generator produces increasingly difficult samples for the discriminator to accelerate optimizing the discrimination objective.

Self-supervised Graph Learning for Recommendation

1 code implementation21 Oct 2020 Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, Xing Xie

In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation.

Graph Learning Representation Learning +1

PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision

1 code implementation Findings of the Association for Computational Linguistics 2020 Chuhan Wu, Fangzhao Wu, Tao Qi, Jianxun Lian, Yongfeng Huang, Xing Xie

Motivated by pre-trained language models which are pre-trained on large-scale unlabeled corpus to empower many downstream tasks, in this paper we propose to pre-train user models from large-scale unlabeled user behaviors data.

FedCTR: Federated Native Ad CTR Prediction with Multi-Platform User Behavior Data

1 code implementation23 Jul 2020 Chuhan Wu, Fangzhao Wu, Tao Di, Yongfeng Huang, Xing Xie

On each platform a local user model is used to learn user embeddings from the local user behaviors on that platform.

Click-Through Rate Prediction Privacy Preserving

Fine-grained Interest Matching for Neural News Recommendation

no code implementations ACL 2020 Heyuan Wang, Fangzhao Wu, Zheng Liu, Xing Xie

Existing studies generally represent each user as a single vector and then match the candidate news vector, which may lose fine-grained information for recommendation.

News Recommendation

Graph Neural News Recommendation with Unsupervised Preference Disentanglement

1 code implementation ACL 2020 Linmei Hu, Siyong Xu, Chen Li, Cheng Yang, Chuan Shi, Nan Duan, Xing Xie, Ming Zhou

Furthermore, the learned representations are disentangled with latent preference factors by a neighborhood routing algorithm, which can enhance expressiveness and interpretability.

Disentanglement News Recommendation

FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning

no code implementations30 Jun 2020 Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, Xing Xie

In this paper, we propose a fairness-aware news recommendation approach with decomposed adversarial learning and orthogonality regularization, which can alleviate unfairness in news recommendation brought by the biases of sensitive user attributes.

Fairness News Recommendation

Lightrec: A memory and search-efficient recommender system

1 code implementation International World Wide Web Conference 2020 Defu Lian, Haoyu Wang, Zheng Liu, Jianxun Lian, Enhong Chen, Xing Xie

On top of such a structure, LightRec will have an item represented as additive composition of B codewords, which are optimally selected from each of the codebooks.

Recommendation Systems

FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning

no code implementations20 Mar 2020 Suyu Ge, Fangzhao Wu, Chuhan Wu, Tao Qi, Yongfeng Huang, Xing Xie

Since the labeled data in different platforms usually has some differences in entity type and annotation criteria, instead of constraining different platforms to share the same model, we decompose the medical NER model in each platform into a shared module and a private module.

Federated Learning Medical Named Entity Recognition +4

A Survey on Knowledge Graph-Based Recommender Systems

no code implementations28 Feb 2020 Qingyu Guo, Fuzhen Zhuang, Chuan Qin, HengShu Zhu, Xing Xie, Hui Xiong, Qing He

On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation.

Explainable Recommendation Recommendation Systems

Graph Convolution Machine for Context-aware Recommender System

1 code implementation30 Jan 2020 Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, Xing Xie

The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on user-item graph.

Collaborative Filtering Recommendation Systems

Neural News Recommendation with Heterogeneous User Behavior

no code implementations IJCNLP 2019 Chuhan Wu, Fangzhao Wu, Mingxiao An, Tao Qi, Jianqiang Huang, Yongfeng Huang, Xing Xie

In the user representation module, we propose an attentive multi-view learning framework to learn unified representations of users from their heterogeneous behaviors such as search queries, clicked news and browsed webpages.

MULTI-VIEW LEARNING News Recommendation

Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network

no code implementations IJCNLP 2019 Chuhan Wu, Fangzhao Wu, Tao Qi, Suyu Ge, Yongfeng Huang, Xing Xie

In the review content-view, we propose to use a hierarchical model to first learn sentence representations from words, then learn review representations from sentences, and finally learn user/item representations from reviews.

MULTI-VIEW LEARNING Representation Learning

NPA: Neural News Recommendation with Personalized Attention

no code implementations12 Jul 2019 Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie

Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word- and news-level attention mechanism to help our model attend to important words and news articles.

Informativeness News Recommendation

Neural News Recommendation with Attentive Multi-View Learning

4 code implementations12 Jul 2019 Chuhan Wu, Fangzhao Wu, Mingxiao An, Jianqiang Huang, Yongfeng Huang, Xing Xie

In the user encoder we learn the representations of users based on their browsed news and apply attention mechanism to select informative news for user representation learning.

MULTI-VIEW LEARNING News Recommendation +2

Neural News Recommendation with Long- and Short-term User Representations

1 code implementation ACL 2019 Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie

In this paper, we propose a neural news recommendation approach which can learn both long- and short-term user representations.

News Recommendation

Exploring Sequence-to-Sequence Learning in Aspect Term Extraction

no code implementations ACL 2019 Dehong Ma, Sujian Li, Fangzhao Wu, Xing Xie, Houfeng Wang

Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence and is usually modeled as a sequence labeling problem.

Term Extraction

Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems

no code implementations24 Jun 2019 Xiao Zhou, Danyang Liu, Jianxun Lian, Xing Xie

The success of recommender systems in modern online platforms is inseparable from the accurate capture of users' personal tastes.

Metric Learning Recommendation Systems +1

Collaborative Translational Metric Learning

1 code implementation4 Jun 2019 Chanyoung Park, Donghyun Kim, Xing Xie, Hwanjo Yu

We also conduct extensive qualitative evaluations on the translation vectors learned by our proposed method to ascertain the benefit of adopting the translation mechanism for implicit feedback-based recommendations.

Knowledge Graph Embedding Metric Learning +1

Personalized Multimedia Item and Key Frame Recommendation

no code implementations1 Jun 2019 Le Wu, Lei Chen, Yonghui Yang, Richang Hong, Yong Ge, Xing Xie, Meng Wang

We argue that the key challenge of this problem lies in discovering users' visual profiles for key frame recommendation, as most recommendation models would fail without any users' fine-grained image behavior.

NRPA: Neural Recommendation with Personalized Attention

5 code implementations29 May 2019 Hongtao Liu, Fangzhao Wu, Wenjun Wang, Xianchen Wang, Pengfei Jiao, Chuhan Wu, Xing Xie

In this paper we propose a neural recommendation approach with personalized attention to learn personalized representations of users and items from reviews.

Informativeness News Recommendation +1

Neural Review Rating Prediction with Hierarchical Attentions and Latent Factors

no code implementations29 May 2019 Xianchen Wang, Hongtao Liu, Peiyi Wang, Fangzhao Wu, Hongyan Xu, Wenjun Wang, Xing Xie

In this paper, we propose a hierarchical attention model fusing latent factor model for rating prediction with reviews, which can focus on important words and informative reviews.


Transcribing Content from Structural Images with Spotlight Mechanism

no code implementations27 May 2019 Yu Yin, Zhenya Huang, Enhong Chen, Qi Liu, Fuzheng Zhang, Xing Xie, Guoping Hu

Then, we decide "what-to-write" by developing a GRU based network with the spotlight areas for transcribing the content accordingly.

Neural Chinese Named Entity Recognition via CNN-LSTM-CRF and Joint Training with Word Segmentation

1 code implementation26 Apr 2019 Fangzhao Wu, Junxin Liu, Chuhan Wu, Yongfeng Huang, Xing Xie

Besides, the training data for CNER in many domains is usually insufficient, and annotating enough training data for CNER is very expensive and time-consuming.

Chinese Named Entity Recognition named-entity-recognition +1

Neural Chinese Word Segmentation with Lexicon and Unlabeled Data via Posterior Regularization

no code implementations26 Apr 2019 Junxin Liu, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie

Luckily, the unlabeled data is usually easy to collect and many high-quality Chinese lexicons are off-the-shelf, both of which can provide useful information for CWS.

Chinese Word Segmentation

Knowledge Graph Convolutional Networks for Recommender Systems

8 code implementations18 Mar 2019 Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo

To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information.

Click-Through Rate Prediction Collaborative Filtering +3

Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

3 code implementations23 Jan 2019 Hongwei Wang, Fuzheng Zhang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo

Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems.

Collaborative Filtering Knowledge Graph Embedding +4

Session-based Recommendation with Graph Neural Networks

7 code implementations1 Nov 2018 Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, Tieniu Tan

To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i. e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity.

Session-Based Recommendations

Detecting Tweets Mentioning Drug Name and Adverse Drug Reaction with Hierarchical Tweet Representation and Multi-Head Self-Attention

1 code implementation WS 2018 Chuhan Wu, Fangzhao Wu, Junxin Liu, Sixing Wu, Yongfeng Huang, Xing Xie

This paper describes our system for the first and third shared tasks of the third Social Media Mining for Health Applications (SMM4H) workshop, which aims to detect the tweets mentioning drug names and adverse drug reactions.

MOBA-Slice: A Time Slice Based Evaluation Framework of Relative Advantage between Teams in MOBA Games

no code implementations22 Jul 2018 Lijun Yu, Dawei Zhang, Xiangqun Chen, Xing Xie

In this paper, we introduce MOBA-Slice, a time slice based evaluation framework of relative advantage between teams in MOBA games.

Neural Chinese Word Segmentation with Dictionary Knowledge

no code implementations11 Jul 2018 Junxin Liu, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, Xing Xie

The experimental results on two benchmark datasets validate that our approach can effectively improve the performance of Chinese word segmentation, especially when training data is insufficient.

Chinese Word Segmentation Multi-Task Learning

A Hierarchical Attention Model for Social Contextual Image Recommendation

1 code implementation3 Jun 2018 Le Wu, Lei Chen, Richang Hong, Yanjie Fu, Xing Xie, Meng Wang

After that, we design a hierarchical attention network that naturally mirrors the hierarchical relationship (elements in each aspects level, and the aspect level) of users' latent interests with the identified key aspects.

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

16 code implementations14 Mar 2018 Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, Guangzhong Sun

On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly.

Click-Through Rate Prediction Recommendation Systems

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

9 code implementations9 Mar 2018 Hongwei Wang, Fuzheng Zhang, Jialin Wang, Miao Zhao, Wenjie Li, Xing Xie, Minyi Guo

To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance.

Click-Through Rate Prediction Collaborative Filtering +2

DKN: Deep Knowledge-Aware Network for News Recommendation

4 code implementations25 Jan 2018 Hongwei Wang, Fuzheng Zhang, Xing Xie, Minyi Guo

To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation.

Click-Through Rate Prediction Common Sense Reasoning +2

SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction

1 code implementation3 Dec 2017 Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi Liu

First, due to the lack of explicit sentiment links in mainstream social networks, we establish a labeled heterogeneous sentiment dataset which consists of users' sentiment relation, social relation and profile knowledge by entity-level sentiment extraction method.

Link Prediction Network Embedding +1

A World of Difference: Divergent Word Interpretations among People

no code implementations8 Mar 2017 Tianran Hu, Ruihua Song, Maya Abtahian, Philip Ding, Xing Xie, Jiebo Luo

We propose an approach that quantifies semantic differences in interpretations among different groups of people.

T-Drive: Driving Directions Based on Taxi Trajectories

no code implementations ACM SIGSPATIAL GIS 2010 2010 Jing Yuan, Yu Zheng, Chengyang Zhang, Wenlei Xie, Xing Xie, Guangzhong Sun, Yan Huang

GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge.

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