Search Results for author: Xing Xie

Found 91 papers, 37 papers with code

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

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.

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

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

PrivateRec: Differentially Private Training and Serving for Federated News Recommendation

no code implementations18 Apr 2022 Ruixuan Liu, Fangzhao Wu, Chuhan Wu, Yanlin Wang, Yang Cao, Lingjuan Lyu, Weike Pan, Yun Chen, Hong Chen, Xing Xie

In this paper, we propose a unified federated news recommendation method for effective and privacy-preserving model training and online serving with differential privacy guarantees.

Federated Learning News Recommendation

ProFairRec: Provider Fairness-aware News Recommendation

no code implementations10 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.

Pretrained Language Models

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

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

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 +1

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

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

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 +1

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

8 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 Pretrained Language Models +2

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

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 Pretrained Language Models +1

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.

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

no code implementations23 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

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

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

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

Privacy-Preserving News Recommendation Model Learning

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

Extensive experiments on a real-world dataset show the effectiveness of our method in news recommendation model training with privacy protection.

Federated Learning News Recommendation

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 +1

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.

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

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

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

A Novel User Representation Paradigm for Making Personalized Candidate Retrieval

no code implementations15 Jul 2019 Zheng Liu, Yu Xing, Jianxun Lian, Defu Lian, Ziyao Li, Xing Xie

Our work is undergoing a anonymous review, and it will soon be released after the notification.

Metric Learning

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

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

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

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

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.

Frame

NRPA: Neural Recommendation with Personalized Attention

4 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.

Informativeness

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

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

7 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 +2

Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation

4 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

10 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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