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.
no code implementations • 27 Jan 2025 • Wenna Lai, Haoran Xie, Guandong Xu, Qing Li
The most challenging task, aspect sentiment quad prediction (ASQP), predicts these elements simultaneously, hindered by difficulties in accurately coupling different sentiment elements.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
1 code implementation • 7 Jan 2025 • Zhangqian Bi, Yao Wan, Zhaoyang Chu, Yufei Hu, Junyi Zhang, Hongyu Zhang, Guandong Xu, Hai Jin
Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, code summarization, and vulnerability detection.
no code implementations • 12 Dec 2024 • Wenna Lai, Haoran Xie, Guandong Xu, Qing Li
Implicit sentiment analysis (ISA) presents significant challenges due to the absence of salient cue words.
no code implementations • 2 Nov 2024 • Jiahui Jin, Yi Hong, Guandong Xu, Jinghui Zhang, Jun Tang, Hancheng Wang
Furthermore, we introduce a type-aware spatiotemporal point process that learns crime-evolving features, measuring the risk of specific crime types at a given time and location by considering the frequency of past crime events.
1 code implementation • 26 Oct 2024 • Sixu An, Xiangguo Sun, Yicong Li, Yu Yang, Guandong Xu
Personality analysis from online short videos has gained prominence due to its applications in personalized recommendation systems, sentiment analysis, and human-computer interaction.
no code implementations • 2 Sep 2024 • Haoran Yang, Xiangyu Zhao, Sirui Huang, Qing Li, Guandong Xu
Graph Contrastive Learning (GCL) is a potent paradigm for self-supervised graph learning that has attracted attention across various application scenarios.
no code implementations • 27 Aug 2024 • Zihao Li, Chao Yang, Yakun Chen, Xianzhi Wang, Hongxu Chen, Guandong Xu, Lina Yao, Quan Z. Sheng
Recent years have witnessed the remarkable success of recommendation systems (RSs) in alleviating the information overload problem.
1 code implementation • 22 Aug 2024 • Sirui Huang, Yanggan Gu, Xuming Hu, Zhonghao Li, Qing Li, Guandong Xu
This benchmark allows us to investigate the capability of LLMs across five factual tasks derived from the unique characteristics of structural facts.
no code implementations • 24 Jul 2024 • Hamad Zogan, Imran Razzak, Shoaib Jameel, Guandong Xu
As a result, \texttt{NarrationDep} is characterized by a novel two-layer deep learning model: the first layer models using social media text posts, and the second layer learns semantic representations of tweets associated with a cluster.
no code implementations • 2 Jul 2024 • Wenna Lai, Haoran Xie, Guandong Xu, Qing Li
To identify implicit sentiment with reliable reasoning, this study proposes RVISA, a two-stage reasoning framework that harnesses the generation ability of DO LLMs and the reasoning ability of ED LLMs to train an enhanced reasoner.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+4
1 code implementation • 19 Jun 2024 • Yicong Li, Yu Yang, Jiannong Cao, Shuaiqi Liu, Haoran Tang, Guandong Xu
We first identify biased structural evolutions in a dynamic graph based on the evolving trend of vertex degree and then propose FairDGE, the first structurally Fair Dynamic Graph Embedding algorithm.
no code implementations • 29 May 2024 • Xueyao Sun, Kaize Shi, Haoran Tang, Guandong Xu, Qing Li
Large language models (LLMs) can elicit social bias during generations, especially when inference with toxic prompts.
no code implementations • 6 May 2024 • Kaize Shi, Xueyao Sun, Qing Li, Guandong Xu
The proposed algorithm compresses the cluttered raw retrieved documents into a compact set of crucial concepts distilled from the informative nodes of AMR by referring to reliable linguistic features.
Abstract Meaning Representation
Open-Domain Question Answering
+2
1 code implementation • 26 Apr 2024 • Yang Wu, Yao Wan, Hongyu Zhang, Yulei Sui, Wucai Wei, Wei Zhao, Guandong Xu, Hai Jin
In particular, we first explore the ways of transforming structured tabular data into sequential text prompts, as to feed them into LLMs and analyze which table content contributes most to the NL2Vis.
1 code implementation • 24 Apr 2024 • Zhaoyang Chu, Yao Wan, Qian Li, Yang Wu, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin
We argue that these factual reasoning-based explanations cannot answer critical what-if questions: What would happen to the GNN's decision if we were to alter the code graph into alternative structures?
no code implementations • 10 Apr 2024 • Kaixi Hu, Lin Li, Qing Xie, Xiaohui Tao, Guandong Xu
Granularity and accuracy are two crucial factors for crime event prediction.
no code implementations • 18 Feb 2024 • Yakun Chen, Kaize Shi, Zhangkai Wu, Juan Chen, Xianzhi Wang, Julian McAuley, Guandong Xu, Shui Yu
Spatiotemporal data analysis is pivotal across various domains, such as transportation, meteorology, and healthcare.
no code implementations • 26 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.
no code implementations • 12 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.
no code implementations • 11 Jan 2024 • Yicong Li, Xiangguo Sun, Hongxu Chen, Sixiao Zhang, Yu Yang, Guandong Xu
Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability.
no code implementations • 8 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.
no code implementations • 30 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.
1 code implementation • 18 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.
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.
1 code implementation • Pacific-Asia Conference on Knowledge Discovery and Data Mining 2023 • David Hason Rudd, Huan Huo, Guandong Xu
Emotion recognition (ER) from speech signals is a robust approach since it cannot be imitated like facial expression or text based sentiment analysis.
Ranked #1 on
Speech Emotion Recognition
on EMODB
(using extra training data)
no code implementations • 3 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.
no code implementations • 24 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.
1 code implementation • 24 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.
no code implementations • 9 Aug 2023 • Kaize Shi, Xueyao Sun, Dingxian Wang, Yinlin Fu, Guandong Xu, Qing Li
E-commerce authoring entails creating engaging, diverse, and targeted content to enhance preference elicitation and retrieval experience.
1 code implementation • 10 Jul 2023 • Xiangmeng Wang, Qian Li, Dianer Yu, Wei Huang, Guandong Xu
In this work, we propose to integrate causal modeling with the learning process of GCN-based GCF models, leveraging causality-aware graph embeddings to capture complex causal relations in recommendations.
no code implementations • 10 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.
no code implementations • 23 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.
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.
no code implementations • 8 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).
1 code implementation • 26 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.
1 code implementation • 26 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).
1 code implementation • 8 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.
no code implementations • 6 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.
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.
no code implementations • 24 Jul 2022 • Haoran Yang, Xiangyu Zhao, Muyang Li, Hongxu Chen, Guandong Xu
Currently, graph learning models are indispensable tools to help researchers explore graph-structured data.
1 code implementation • 14 Jul 2022 • Xiangmeng Wang, Qian Li, Dianer Yu, Guandong Xu
We also deploy the explanation policy to a recommendation model to enhance the recommendation.
no code implementations • 1 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.
1 code implementation • 23 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).
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.
1 code implementation • 17 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.
1 code implementation • 14 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.
no code implementations • 5 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.
1 code implementation • 20 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.
no code implementations • 19 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.
1 code implementation • 19 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.
no code implementations • 24 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.
no code implementations • 7 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.
no code implementations • CVPR 2021 • Qian Li, Zhichao Wang, Gang Li, Jun Pang, Guandong Xu
Sinkhorn divergence has become a very popular metric to compare probability distributions in optimal transport.
no code implementations • 28 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.
no code implementations • 27 May 2021 • Tri Dung Duong, Qian Li, Guandong Xu
Central to these applications is the treatment effect estimation of intervention strategies.
no code implementations • 23 May 2021 • Hamad Zogan, Imran Razzak, Shoaib Jameel, Guandong Xu
Twitter is currently a popular online social media platform which allows users to share their user-generated content.
1 code implementation • 19 May 2021 • Sixiao Zhang, Hongxu Chen, Xiao Ming, Lizhen Cui, Hongzhi Yin, Guandong Xu
Hyperbolic space and hyperbolic embeddings are becoming a popular research field for recommender systems.
no code implementations • 17 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.
1 code implementation • 3 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.
no code implementations • 19 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).
no code implementations • 15 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.
no code implementations • 23 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.
1 code implementation • 5 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
no code implementations • 2 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.
no code implementations • 14 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.
no code implementations • 3 Jul 2020 • Hamad Zogan, Imran Razzak, Xianzhi Wang, Shoaib Jameel, Guandong Xu
Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction.
no code implementations • 27 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
no code implementations • 26 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.
no code implementations • 10 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.
2 code implementations • 17 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.
no code implementations • 28 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.
no code implementations • 24 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.
no code implementations • 30 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.
no code implementations • 18 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.
no code implementations • 25 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.
no code implementations • 8 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.