Search Results for author: Guandong Xu

Found 61 papers, 20 papers with code

Harnessing Privileged Information for Hyperbole Detection

no code implementations ALTA 2021 Rhys Biddle, Maciek Rybinski, Qian Li, Cecile Paris, Guandong Xu

The detection of hyperbole is an important stepping stone to understanding the intentions of a hyperbolic utterance.

Challenging Low Homophily in Social Recommendation

no code implementations26 Jan 2024 Wei Jiang, Xinyi Gao, Guandong Xu, Tong Chen, Hongzhi Yin

To comprehensively extract preference-aware homophily information latent in the social graph, we propose Social Heterophily-alleviating Rewiring (SHaRe), a data-centric framework for enhancing existing graph-based social recommendation models.

Contrastive Learning

APLe: Token-Wise Adaptive for Multi-Modal Prompt Learning

no code implementations12 Jan 2024 Guiming Cao, Kaize Shi, Hong Fu, Huaiwen Zhang, Guandong Xu

Pre-trained Vision-Language (V-L) models set the benchmark for generalization to downstream tasks among the noteworthy contenders.

IDoFew: Intermediate Training Using Dual-Clustering in Language Models for Few Labels Text Classification

no code implementations8 Jan 2024 Abdullah Alsuhaibani, Hamad Zogan, Imran Razzak, Shoaib Jameel, Guandong Xu

Although some approaches have attempted to address this problem through single-stage clustering as an intermediate training step coupled with a pre-trained language model, which generates pseudo-labels to improve classification, these methods are often error-prone due to the limitations of the clustering algorithms.

Clustering Language Modelling +2

Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit

no code implementations30 Dec 2023 Yao Wan, Yang He, Zhangqian Bi, JianGuo Zhang, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin, Philip S. Yu

We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models.

Representation Learning

Predicting Financial Literacy via Semi-supervised Learning

1 code implementation18 Dec 2023 David Hason Rudd, Huan Huo, Guandong Xu

We propose the SMOGN-COREG model for semi-supervised regression, applying SMOGN to deal with unbalanced datasets and a nonparametric multi-learner co-regression (COREG) algorithm for labeling.


Leveraged Mel spectrograms using Harmonic and Percussive Components in Speech Emotion Recognition

1 code implementation Pacific-Asia Conference on Knowledge Discovery and Data Mining 2022 David Hason Rudd, Huan Huo, Guandong Xu

We attempt to leverage the Mel spectrogram by decomposing distinguishable acoustic features for exploitation in our proposed architecture, which includes a novel feature map generator algorithm, a CNN-based network feature extractor and a multi-layer perceptron (MLP) classifier.

Data Augmentation Speech Emotion Recognition

Churn Prediction via Multimodal Fusion Learning:Integrating Customer Financial Literacy, Voice, and Behavioral Data

no code implementations3 Dec 2023 David Hason Rudd, Huan Huo, Md Rafiqul Islam, Guandong Xu

Our novel approach demonstrates a marked improvement in churn prediction, achieving a test accuracy of 91. 2%, a Mean Average Precision (MAP) score of 66, and a Macro-Averaged F1 score of 54 through the proposed hybrid fusion learning technique compared with late fusion and baseline models.

Speech Emotion Recognition

GATGPT: A Pre-trained Large Language Model with Graph Attention Network for Spatiotemporal Imputation

no code implementations24 Nov 2023 Yakun Chen, Xianzhi Wang, Guandong Xu

The objective of spatiotemporal imputation is to estimate these missing values by understanding the inherent spatial and temporal relationships in the observed multivariate time series.

Graph Attention Imputation +4

Towards Communication-Efficient Model Updating for On-Device Session-Based Recommendation

1 code implementation24 Aug 2023 Xin Xia, Junliang Yu, Guandong Xu, Hongzhi Yin

On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy.

Session-Based Recommendations

Counterfactual Explanation for Fairness in Recommendation

no code implementations10 Jul 2023 Xiangmeng Wang, Qian Li, Dianer Yu, Qing Li, Guandong Xu

The counterfactual explanations help to provide rational and proximate explanations for model fairness, while the attentive action pruning narrows the search space of attributes.

Attribute Causal Inference +4

Causal Neural Graph Collaborative Filtering

no code implementations10 Jul 2023 Xiangmeng Wang, Qian Li, Dianer Yu, Wei Huang, Guandong Xu

One classical approach in GCF is to learn user and item embeddings by modeling complex graph relations and utilizing these embeddings for CF models.

Collaborative Filtering Graph Learning +2

Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions

no code implementations23 Apr 2023 David Hason Rudd, Huan Huo, Guandong Xu

Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period.

Causal Discovery

Causal Analysis of Customer Churn Using Deep Learning

1 code implementation International Conference on Digital Society and Intelligent Systems (DSInS) 2021 David Hason Rudd, Huan Huo, Guandong Xu

Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives.

Marketing Sequential Pattern Mining

Model-Agnostic Decentralized Collaborative Learning for On-Device POI Recommendation

no code implementations8 Apr 2023 Jing Long, Tong Chen, Nguyen Quoc Viet Hung, Guandong Xu, Kai Zheng, Hongzhi Yin

In light of this, We propose a novel on-device POI recommendation framework, namely Model-Agnostic Collaborative learning for on-device POI recommendation (MAC), allowing users to customize their own model structures (e. g., dimension \& number of hidden layers).

Knowledge Distillation Privacy Preserving

Achieving Counterfactual Fairness with Imperfect Structural Causal Model

1 code implementation26 Mar 2023 Tri Dung Duong, Qian Li, Guandong Xu

Counterfactual fairness alleviates the discrimination between the model prediction toward an individual in the actual world (observational data) and that in counterfactual world (i. e., what if the individual belongs to other sensitive groups).

counterfactual Counterfactual Inference +1

CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows

1 code implementation26 Mar 2023 Tri Dung Duong, Qian Li, Guandong Xu

Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome.

counterfactual Counterfactual Explanation +1

COV19IR : COVID-19 Domain Literature Information Retrieval

1 code implementation8 Nov 2022 Arusarka Bose, Zili Zhou, Guandong Xu

Increasing number of COVID-19 research literatures cause new challenges in effective literature screening and COVID-19 domain knowledge aware Information Retrieval.

Information Retrieval Question Answering +1

Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks

no code implementations6 Sep 2022 Dawei Xu, Haoran Yang, Marian-Andrei Rizoiu, Guandong Xu

In this paper, we study the automation risk of occupations by performing a classification task between automated and non-automated occupations.

Decision Making

Improved Churn Causal Analysis Through Restrained High‑Dimensional Feature Space Efects in Financial Institutions

1 code implementation Human-Centric Intelligent Systems 2022 David Hason Rudd, Huan Huo, Guandong Xu

We combine different algorithms including the SMOTE, ensemble ANN, and Bayesian networks to address churn prediction problems on a massive and high-dimensional finance data that is usually generated in financial institutions due to employing interval-based features used in Customer Relationship Management systems.

Causal Discovery Management

Reinforced Path Reasoning for Counterfactual Explainable Recommendation

1 code implementation14 Jul 2022 Xiangmeng Wang, Qian Li, Dianer Yu, Guandong Xu

We also deploy the explanation policy to a recommendation model to enhance the recommendation.

Attribute counterfactual +2

Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning

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

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

Contrastive Learning counterfactual +2

On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation

1 code implementation23 Apr 2022 Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Guandong Xu, Nguyen Quoc Viet Hung

Meanwhile, to compensate for the capacity loss caused by compression, we develop a self-supervised knowledge distillation framework which enables the compressed model (student) to distill the essential information lying in the raw data, and improves the long-tail item recommendation through an embedding-recombination strategy with the original model (teacher).

Knowledge Distillation Recommendation Systems +1

DialMed: A Dataset for Dialogue-based Medication Recommendation

1 code implementation COLING 2022 Zhenfeng He, Yuqiang Han, Zhenqiu Ouyang, Wei Gao, Hongxu Chen, Guandong Xu, Jian Wu

Therefore, we make the first attempt to recommend medications with the conversations between doctors and patients.

Graph Attention

Graph Masked Autoencoders with Transformers

1 code implementation17 Feb 2022 Sixiao Zhang, Hongxu Chen, Haoran Yang, Xiangguo Sun, Philip S. Yu, Guandong Xu

In this paper, we propose Graph Masked Autoencoders (GMAEs), a self-supervised transformer-based model for learning graph representations.

Graph Classification Node Classification

What Do They Capture? -- A Structural Analysis of Pre-Trained Language Models for Source Code

1 code implementation14 Feb 2022 Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin

In this paper, we conduct a thorough structural analysis aiming to provide an interpretation of pre-trained language models for source code (e. g., CodeBERT, and GraphCodeBERT) from three distinctive perspectives: (1) attention analysis, (2) probing on the word embedding, and (3) syntax tree induction.

Code Completion Code Search +1

Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation

no code implementations5 Feb 2022 Xiangmeng Wang, Qian Li, Dianer Yu, Peng Cui, Zhichao Wang, Guandong Xu

Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users' true intent and thus deteriorate the recommendation effectiveness.


Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation

1 code implementation20 Jan 2022 Sixiao Zhang, Hongxu Chen, Xiangguo Sun, Yicong Li, Guandong Xu

Extensive experiments show that our attack outperforms unsupervised baseline attacks and has comparable performance with supervised attacks in multiple downstream tasks including node classification and link prediction.

Adversarial Attack Contrastive Learning +3

Dual Space Graph Contrastive Learning

no code implementations19 Jan 2022 Haoran Yang, Hongxu Chen, Shirui Pan, Lin Li, Philip S. Yu, Guandong Xu

In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.

Contrastive Learning Graph Learning +1

Cross-Language Binary-Source Code Matching with Intermediate Representations

1 code implementation19 Jan 2022 Yi Gui, Yao Wan, Hongyu Zhang, Huifang Huang, Yulei Sui, Guandong Xu, Zhiyuan Shao, Hai Jin

Binary-source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment.

Malware Detection

Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation

no code implementations24 Nov 2021 Yicong Li, Hongxu Chen, Yile Li, Lin Li, Philip S. Yu, Guandong Xu

Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs.

Explainable Recommendation Knowledge Graphs +3

Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation

no code implementations7 Sep 2021 Haoran Yang, Hongxu Chen, Lin Li, Philip S. Yu, Guandong Xu

They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user's behavioral patterns which will be used in downstream recommendation tasks.

Contrastive Learning Multi-Task Learning +1

Stochastic Intervention for Causal Inference via Reinforcement Learning

no code implementations28 May 2021 Tri Dung Duong, Qian Li, Guandong Xu

In our study, we advance the causal inference research by proposing a new effective framework to estimate the treatment effect on stochastic intervention.

Causal Inference Decision Making +2

Stochastic Intervention for Causal Effect Estimation

no code implementations27 May 2021 Tri Dung Duong, Qian Li, Guandong Xu

Central to these applications is the treatment effect estimation of intervention strategies.

Causal Inference Decision Making

Be Causal: De-biasing Social Network Confounding in Recommendation

no code implementations17 May 2021 Qian Li, Xiangmeng Wang, Guandong Xu

A common practice to address MNAR is to treat missing entries from the so-called "exposure" perspective, i. e., modeling how an item is exposed (provided) to a user.

Causal Inference Recommendation Systems +2

Causality-based Counterfactual Explanation for Classification Models

1 code implementation3 May 2021 Tri Dung Duong, Qian Li, Guandong Xu

Accordingly, the gradient-free methods are proposed to handle the categorical variables, which however have several major limitations: 1) causal relationships among features are typically ignored when generating the counterfactuals, possibly resulting in impractical guidelines for decision-makers; 2) the counterfactual explanation algorithm requires a great deal of effort into parameter tuning for dertermining the optimal weight for each loss functions which must be conducted repeatedly for different datasets and settings.

Classification counterfactual +3

TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers

no code implementations19 Apr 2021 Yao Wu, Jian Cao, Guandong Xu, Yudong Tan

In this paper, we consider recommendation scenarios from the perspective of two sides (customers and providers).

Fairness Recommendation Systems

Hyperbolic Neural Collaborative Recommender

no code implementations15 Apr 2021 Anchen Li, Bo Yang, Hongxu Chen, Guandong Xu

In the second phase, we develop a deep framework based on hyperbolic geometry to integrate constructed neighbor sets into recommendation.

Collaborative Filtering Representation Learning

A General Framework for Learning Prosodic-Enhanced Representation of Rap Lyrics

no code implementations23 Mar 2021 Hongru Liang, Haozheng Wang, Qian Li, Jun Wang, Guandong Xu, Jiawei Chen, Jin-Mao Wei, Zhenglu Yang

Learning and analyzing rap lyrics is a significant basis for many web applications, such as music recommendation, automatic music categorization, and music information retrieval, due to the abundant source of digital music in the World Wide Web.

Information Retrieval Music Information Retrieval +3

Temporal Meta-path Guided Explainable Recommendation

1 code implementation5 Jan 2021 Hongxu Chen, Yicong Li, Xiangguo Sun, Guandong Xu, Hongzhi Yin

This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations.

Social and Information Networks

FAST: A Fairness Assured Service Recommendation Strategy Considering Service Capacity Constraint

no code implementations2 Dec 2020 Yao Wu, Jian Cao, Guandong Xu

In this paper, we propose a novel metric Top-N Fairness to measure the individual fairness of multi-round recommendations of services with capacity constraints.

Fairness Recommendation Systems

Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection

no code implementations14 Sep 2020 Jun Yin, Qian Li, Shaowu Liu, Zhiang Wu, Guandong Xu

Our study investigates the spammer detection problem in the context of multi-relation social networks, and makes an attempt to fully exploit the sequences of heterogeneous relations for enhancing the detection accuracy.


Causality Learning: A New Perspective for Interpretable Machine Learning

no code implementations27 Jun 2020 Guandong Xu, Tri Dung Duong, Qian Li, Shaowu Liu, Xianzhi Wang

Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc.

BIG-bench Machine Learning Interpretable Machine Learning +2

Generative Temporal Link Prediction via Self-tokenized Sequence Modeling

no code implementations26 Nov 2019 Yue Wang, Chenwei Zhang, Shen Wang, Philip S. Yu, Lu Bai, Lixin Cui, Guandong Xu

We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks.

Link Prediction

Social Influence-based Attentive Mavens Mining and Aggregative Representation Learning for Group Recommendation

no code implementations10 Aug 2019 Peipei Wang, Lin Li, Yi Yu, Guandong Xu

To tackle the issue of preference aggregation for group recommendation, we propose a novel attentive aggregation representation learning method based on sociological theory for group recommendation, namely SIAGR (short for "Social Influence-based Attentive Group Recommendation"), which takes attention mechanisms and the popular method (BERT) as the aggregation representation for group profile modeling.

Collaborative Filtering Decision Making +2

Improving Automatic Source Code Summarization via Deep Reinforcement Learning

2 code implementations17 Nov 2018 Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, Philip S. Yu

To the best of our knowledge, most state-of-the-art approaches follow an encoder-decoder framework which encodes the code into a hidden space and then decode it into natural language space, suffering from two major drawbacks: a) Their encoders only consider the sequential content of code, ignoring the tree structure which is also critical for the task of code summarization, b) Their decoders are typically trained to predict the next word by maximizing the likelihood of next ground-truth word with previous ground-truth word given.

Code Summarization reinforcement-learning +4

Target-Independent Active Learning via Distribution-Splitting

no code implementations28 Sep 2018 Xiaofeng Cao, Ivor W. Tsang, Xiaofeng Xu, Guandong Xu

By discovering the connections between hypothesis and input distribution, we map the volume of version space into the number density and propose a target-independent distribution-splitting strategy with the following advantages: 1) provide theoretical guarantees on reducing label complexity and error rate as volume-splitting; 2) break the curse of initial hypothesis; 3) provide model guidance for a target-independent AL algorithm in real AL tasks.

Active Learning

A Structured Perspective of Volumes on Active Learning

no code implementations24 Jul 2018 Xiaofeng Cao, Ivor W. Tsang, Guandong Xu

In this paper, we approximate the version space to a structured {hypersphere} that covers most of the hypotheses, and then divide the available AL sampling approaches into two kinds of strategies: Outer Volume Sampling and Inner Volume Sampling.

Active Learning

An Attention-Based Word-Level Interaction Model: Relation Detection for Knowledge Base Question Answering

no code implementations30 Jan 2018 Hongzhi Zhang, Guandong Xu, Xiao Liang, Tinglei Huang, Kun fu

Then, instead of merging the sequence into a single vector with pooling operation, soft alignments between words from the question and the relation are learned.

Knowledge Base Question Answering Relation +2

CSAL: Self-adaptive Labeling based Clustering Integrating Supervised Learning on Unlabeled Data

no code implementations18 Feb 2015 Fangfang Li, Guandong Xu, Longbing Cao

In this paper, we propose an innovative and effective clustering framework based on self-adaptive labeling (CSAL) which integrates clustering and classification on unlabeled data.

Classification Clustering +2

Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval

no code implementations25 Sep 2014 Liang Wu, Hui Xiong, Liang Du, Bo Liu, Guandong Xu, Yong Ge, Yanjie Fu, Yuanchun Zhou, Jianhui Li

Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes.

Information Retrieval Metric Learning +1

Coupled Item-based Matrix Factorization

no code implementations8 Apr 2014 Fangfang Li, Guandong Xu, Longbing Cao

The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF), mainly rely on the user-item rating matrix, which sometimes is not informative enough for predicting recommendations.

Attribute Collaborative Filtering +1

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