Search Results for author: Fuzhen Zhuang

Found 48 papers, 21 papers with code

Selective Fairness in Recommendation via Prompts

no code implementations10 May 2022 Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xiang Ao, Xu Zhang, Leyu Lin, Qing He

In this work, we define the selective fairness task, where users can flexibly choose which sensitive attributes should the recommendation model be bias-free.

Fairness Sequential Recommendation

Exploiting Global and Local Hierarchies for Hierarchical Text Classification

no code implementations5 May 2022 Ting Jiang, Deqing Wang, Leilei Sun, Zhongzhi Chen, Fuzhen Zhuang, Qinghong Yang

Contrary to global hierarchy, local hierarchy as the structured target labels hierarchy corresponding to each text sample is dynamic and relevant to text samples, which is ignored in previous methods.

Multi Label Text Classification Multi-Label Text Classification

Generalizing to the Future: Mitigating Entity Bias in Fake News Detection

1 code implementation20 Apr 2022 Yongchun Zhu, Qiang Sheng, Juan Cao, Shuokai Li, Danding Wang, Fuzhen Zhuang

In this paper, we propose an entity debiasing framework (\textbf{ENDEF}) which generalizes fake news detection models to the future data by mitigating entity bias from a cause-effect perspective.

Fake News Detection

User-Centric Conversational Recommendation with Multi-Aspect User Modeling

1 code implementation20 Apr 2022 Shuokai Li, Ruobing Xie, Yongchun Zhu, Xiang Ao, Fuzhen Zhuang, Qing He

In this work, we highlight that the user's historical dialogue sessions and look-alike users are essential sources of user preferences besides the current dialogue session in CRS.

Dialogue Generation Dialogue Understanding +1

Multi-view Multi-behavior Contrastive Learning in Recommendation

1 code implementation20 Mar 2022 Yiqing Wu, Ruobing Xie, Yongchun Zhu, Xiang Ao, Xin Chen, Xu Zhang, Fuzhen Zhuang, Leyu Lin, Qing He

We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consider both individual sequence view and global graph view in multi-behavior modeling, and (3) capture the fine-grained differences between multiple behaviors of a user.

Contrastive Learning

Bankruptcy Prediction via Mixing Intra-Risk and Spillover-Risk

1 code implementation1 Feb 2022 Yu Zhao, Shaopeng Wei, Yu Guo, Qing Yang, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou

Afterward, we propose an enterprise spillover-risk encoder based on enterprise relational information from the enterprise knowledge graph for its spillover-risk embedding.

PromptBERT: Improving BERT Sentence Embeddings with Prompts

1 code implementation12 Jan 2022 Ting Jiang, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Liangjie Zhang, Qi Zhang

To this end, we propose a prompt based sentence embeddings method which can reduce token embeddings biases and make the original BERT layers more effective.

Denoising Semantic Similarity +2

Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks

1 code implementation11 Jan 2022 Yu Zhao, Huaming Du, Ying Liu, Shaopeng Wei, Xingyan Chen, Fuzhen Zhuang, Qing Li, Ji Liu, Gang Kou

Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets.

Stock Prediction

Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection

no code implementations4 Jan 2022 Yongchun Zhu, Dongbo Xi, Bowen Song, Fuzhen Zhuang, Shuai Chen, Xi Gu, Qing He

Thus, in this paper, we further propose a transfer framework to tackle the cross-domain fraud detection problem, which aims to transfer knowledge from existing domains (source domains) with enough and mature data to improve the performance in the new domain (target domain).

Fraud Detection

Multi-Representation Adaptation Network for Cross-domain Image Classification

1 code implementation4 Jan 2022 Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Jingwu Chen, Zhiping Shi, Wenjuan Wu, Qing He

Based on this, we present Multi-Representation Adaptation Network (MRAN) to accomplish the cross-domain image classification task via multi-representation alignment which can capture the information from different aspects.

Domain Adaptation Image Classification +1

Aligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources

1 code implementation4 Jan 2022 Yongchun Zhu, Fuzhen Zhuang, Deqing Wang

However, in the practical scenario, labeled data can be typically collected from multiple diverse sources, and they might be different not only from the target domain but also from each other.

Image Classification Multi-Source Unsupervised Domain Adaptation +1

Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification

no code implementations31 Dec 2021 Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, Jingjing Gu, Hui Xiong, Qing He

Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users' dynamic preferences.

Domain Adaptation with Category Attention Network for Deep Sentiment Analysis

no code implementations31 Dec 2021 Dongbo Xi, Fuzhen Zhuang, Ganbin Zhou, Xiaohu Cheng, Fen Lin, Qing He

Domain adaptation tasks such as cross-domain sentiment classification aim to utilize existing labeled data in the source domain and unlabeled or few labeled data in the target domain to improve the performance in the target domain via reducing the shift between the data distributions.

Domain Adaptation Sentiment Analysis

Neural Hierarchical Factorization Machines for User's Event Sequence Analysis

no code implementations31 Dec 2021 Dongbo Xi, Fuzhen Zhuang, Bowen Song, Yongchun Zhu, Shuai Chen, Dan Hong, Tao Chen, Xi Gu, Qing He

Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance.

Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification

no code implementations31 Dec 2021 Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, HengShu Zhu, Pengpeng Zhao, Chang Tan, Qing He

To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users.

Recommendation Systems

Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement

no code implementations27 Dec 2021 Fuwei Zhang, Zhao Zhang, Xiang Ao, Dehong Gao, Fuzhen Zhuang, Yi Wei, Qing He

The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism.

Information Retrieval

Learning Bi-typed Multi-relational Heterogeneous Graph via Dual Hierarchical Attention Networks

1 code implementation24 Dec 2021 Yu Zhao, Shaopeng Wei, Huaming Du, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou

To address this issue, we propose a novel Dual Hierarchical Attention Networks (DHAN) based on the bi-typed multi-relational heterogeneous graphs to learn comprehensive node representations with the intra-class and inter-class attention-based encoder under a hierarchical mechanism.

Graph Learning

Domain-oriented Language Pre-training with Adaptive Hybrid Masking and Optimal Transport Alignment

no code implementations1 Dec 2021 Denghui Zhang, Zixuan Yuan, Yanchi Liu, Hao liu, Fuzhen Zhuang, Hui Xiong, Haifeng Chen

Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge.

Entity Alignment

Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation

1 code implementation NeurIPS 2021 Ying Sun, HengShu Zhu, Chuan Qin, Fuzhen Zhuang, Qing He, Hui Xiong

To this end, in this paper, we aim to discern the decision-making processes of neural networks through a hierarchical voting strategy by developing an explainable deep learning model, namely Voting Transformation-based Explainable Neural Network (VOTEN).

Decision Making

Deep Keyphrase Completion

no code implementations29 Oct 2021 Yu Zhao, Jia Song, Huali Feng, Fuzhen Zhuang, Qing Li, Xiaojie Wang, Ji Liu

Keyphrase provides accurate information of document content that is highly compact, concise, full of meanings, and widely used for discourse comprehension, organization, and text retrieval.

Keyphrase Extraction Keyphrase Generation

Personalized Transfer of User Preferences for Cross-domain Recommendation

1 code implementation21 Oct 2021 Yongchun Zhu, Zhenwei Tang, Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, Qing He

Specifically, a meta network fed with users' characteristic embeddings is learned to generate personalized bridge functions to achieve personalized transfer of preferences for each user.

Recommendation Systems

Improving Non-autoregressive Generation with Mixup Training

1 code implementation21 Oct 2021 Ting Jiang, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Liangjie Zhang, Qi Zhang

While pre-trained language models have achieved great success on various natural language understanding tasks, how to effectively leverage them into non-autoregressive generation tasks remains a challenge.

Natural Language Understanding Paraphrase Generation +1

Deep Subdomain Adaptation Network for Image Classification

1 code implementation17 Jun 2021 Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, Qing He

The adaptation can be achieved easily with most feed-forward network models by extending them with LMMD loss, which can be trained efficiently via back-propagation.

Domain Adaptation Image Classification +3

AMA-GCN: Adaptive Multi-layer Aggregation Graph Convolutional Network for Disease Prediction

no code implementations16 Jun 2021 Hao Chen, Fuzhen Zhuang, Li Xiao, Ling Ma, Haiyan Liu, Ruifang Zhang, Huiqin Jiang, Qing He

The encoder can automatically construct the population graph using phenotypic measures which have a positive impact on the final results, and further realizes the fusion of multimodal information.

Disease Prediction text similarity

Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising

1 code implementation31 May 2021 Yongchun Zhu, Yudan Liu, Ruobing Xie, Fuzhen Zhuang, Xiaobo Hao, Kaikai Ge, Xu Zhang, Leyu Lin, Juan Cao

Besides, MetaHeac has been successfully deployed in WeChat for the promotion of both contents and advertisements, leading to great improvement in the quality of marketing.

Meta-Learning Recommendation Systems

Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising

2 code implementations18 May 2021 Dongbo Xi, Zhen Chen, Peng Yan, Yinger Zhang, Yongchun Zhu, Fuzhen Zhuang, Yu Chen

While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion.

Multi-Task Learning

Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users

no code implementations11 May 2021 Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang, Ruobing Xie, Dongbo Xi, Xu Zhang, Leyu Lin, Qing He

With the advantage of meta learning which has good generalization ability to novel tasks, we propose a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta stage.

Meta-Learning Recommendation Systems

Combat Data Shift in Few-shot Learning with Knowledge Graph

no code implementations27 Jan 2021 Yongchun Zhu, Fuzhen Zhuang, Xiangliang Zhang, Zhiyuan Qi, Zhiping Shi, Juan Cao, Qing He

However, in real-world applications, few-shot learning paradigm often suffers from data shift, i. e., samples in different tasks, even in the same task, could be drawn from various data distributions.

Few-Shot Learning

Modeling Heterogeneous Relations across Multiple Modes for Potential Crowd Flow Prediction

no code implementations18 Jan 2021 Qiang Zhou, Jingjing Gu, Xinjiang Lu, Fuzhen Zhuang, Yanchao Zhao, Qiuhong Wang, Xiao Zhang

Intuitively, the potential crowd flow of the new coming site can be implied by exploring the nearby sites.

LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification

1 code implementation9 Jan 2021 Ting Jiang, Deqing Wang, Leilei Sun, Huayi Yang, Zhengyang Zhao, Fuzhen Zhuang

In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels.

General Classification Multi Label Text Classification +1

E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce

no code implementations7 Sep 2020 Denghui Zhang, Zixuan Yuan, Yanchi Liu, Fuzhen Zhuang, Haifeng Chen, Hui Xiong

Pre-trained language models such as BERT have achieved great success in a broad range of natural language processing tasks.

Aspect Extraction Denoising +3

Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection

no code implementations8 Aug 2020 Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen, Tianyi Zhang, Yuan Qi, Qing He

In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i. e., field value variations and field interactions simultaneously for fraud detection.

Fraud Detection

Graph Factorization Machines for Cross-Domain Recommendation

no code implementations12 Jul 2020 Dongbo Xi, Fuzhen Zhuang, Yongchun Zhu, Pengpeng Zhao, Xiangliang Zhang, Qing He

In this paper, we propose a Graph Factorization Machine (GFM) which utilizes the popular Factorization Machine to aggregate multi-order interactions from neighborhood for recommendation.

Recommendation Systems

Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems

1 code implementation13 Apr 2020 Jingjing Gu, Qiang Zhou, Jingyuan Yang, Yanchi Liu, Fuzhen Zhuang, Yanchao Zhao, Hui Xiong

Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility.

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

Transfer Learning Toolkit: Primers and Benchmarks

1 code implementation20 Nov 2019 Fuzhen Zhuang, Keyu Duan, Tongjia Guo, Yongchun Zhu, Dongbo Xi, Zhiyuan Qi, Qing He

The transfer learning toolkit wraps the codes of 17 transfer learning models and provides integrated interfaces, allowing users to use those models by calling a simple function.

Transfer Learning

A Comprehensive Survey on Transfer Learning

2 code implementations7 Nov 2019 Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, HengShu Zhu, Hui Xiong, Qing He

In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments.

Transfer Learning

Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning

no code implementations11 Oct 2019 Changying Du, Jia He, Changde Du, Fuzhen Zhuang, Qing He, Guoping Long

Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning.


Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling

no code implementations10 Oct 2019 Changying Du, Fuzhen Zhuang, Jia He, Qing He, Guoping Long

In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data.

Knowledge Graph Embedding with Hierarchical Relation Structure

no code implementations EMNLP 2018 Zhao Zhang, Fuzhen Zhuang, Meng Qu, Fen Lin, Qing He

To this end, in this paper, we extend existing KGE models TransE, TransH and DistMult, to learn knowledge representations by leveraging the information from the HRS.

Information Retrieval Knowledge Base Completion +3

Cross-Domain Labeled LDA for Cross-Domain Text Classification

1 code implementation16 Sep 2018 Baoyu Jing, Chenwei Lu, Deqing Wang, Fuzhen Zhuang, Cheng Niu

To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA).

Cross-Domain Text Classification General Classification

The Automatic Identification of Butterfly Species

no code implementations18 Mar 2018 Juanying Xie, Qi Hou, Yinghuan Shi, Lv Peng, Liping Jing, Fuzhen Zhuang, Junping Zhang, Xiaoyang Tang, Shengquan Xu

We delete those species with only one living environment image from data set, then partition the rest images from living environment into two subsets, one used as test subset, the other as training subset respectively combined with all standard pattern butterfly images or the standard pattern butterfly images with the same species of the images from living environment.

Policy Gradients for Contextual Recommendations

no code implementations12 Feb 2018 Feiyang Pan, Qingpeng Cai, Pingzhong Tang, Fuzhen Zhuang, Qing He

We evaluate PGCR on toy datasets as well as a real-world dataset of personalized music recommendations.

Decision Making Multi-Armed Bandits +2

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