Guandong Xu

University of Technology Sydney | UTS · School of Computer Science

The Natural Language to Visualization (NL2Vis) task aims to transform natural-language descriptions into visual representations for a grounded table, enabling users to gain insights from vast amounts of data. Recently, many deep learning-based approaches have been developed for NL2Vis. Despite the considerable efforts made by these approaches, chal...

Large language models (LLMs) can elicit social bias during generations, especially when inference with toxic prompts. Controlling the sensitive attributes in generation encounters challenges in data distribution, generalizability, and efficiency. Specifically, fine-tuning and retrieval demand extensive unbiased corpus, while direct prompting requir...

Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving research community focusing on code intelligence, with efforts ranging from software engineerin...

As a significant application of machine learning in financial scenarios, loan default risk prediction aims to evaluate the client’s default probability. However, most existing deep learning solutions treat each application as an independent individual, neglecting the explicit connections among different application records. Besides, these attempts...

Augmenting Language Models (LMs) with structured knowledge graphs (KGs) aims to leverage structured world knowledge to enhance the capability of LMs to complete knowledge-intensive tasks. However, existing methods are unable to effectively utilize the structured knowledge in a KG due to their inability to capture the rich relational semantics of kn...

Purpose This paper explores the potential for family businesses (FBs) to play a pivotal role in advancing the United Nations (UN) Sustainable Development Goals (SDGs). It seeks to elucidate how FBs' inherent strengths and values can be harnessed to integrate sustainable practices within their operational paradigms. Design/methodology/approach The...

With the introduction of more recent deep learning models such as encoder‐decoder, text generation frameworks have gained a lot of popularity. In Natural Language Generation (NLG), controlling the information and style of the output produced is a crucial and challenging task. The purpose of this paper is to develop informative and controllable text...

Counterfactual explanations interpret the recommendation mechanism by exploring how minimal alterations on items or users affect recommendation decisions. Existing counterfactual explainable approaches face huge search space, and their explanations are either action-based (e.g., user click) or aspect-based (i.e., item description). We believe item...

Compared with only pursuing recommendation accuracy, the explainability of a recommendation model has drawn more attention in recent years. Many graph-based recommendations resort to informative paths with the attention mechanism for the explanation. Unfortunately, these attention weights are intentionally designed for model accuracy but not explai...

In recent times, visual analytics systems (VAS) have been used to solve various complex issues in diverse application domains. Nonetheless, an inherent drawback arises from the insufficient evaluation of VAS, resulting in occasional inaccuracies when it comes to analytical reasoning, information synthesis, and deriving insights from vast, ever-chan...

Improving users’ long-term experience in recommender systems (RS) has become a growing concern for recommendation platforms. Reinforcement learning (RL) is an attractive approach because it can plan and optimize long-term returns sequentially. However, directly applying RL as an online learning method in the RS setting can significantly compromise u...

Temporal knowledge graph (TKG) forecasting is widely used in various fields due to its ability to infer future events based on historical information. Modeling the internal structures and chronological dependencies of historical subgraph sequences has been proven effective. Nevertheless, on the one hand, the TKG forecasting process generally suffers...

Spatiotemporal data analysis is crucial for various fields of applications, such as transportation, healthcare, and meteorology. Spatiotemporal data collected in the real world often contain missing values due to sensor failures or transmission loss. Therefore, spatiotemporal imputation aims to fill in the missing values by leveraging the underlyin...

Multivariate time series classification is crucial for various applications such as activity recognition, disease diagnosis, and brain-computer interfaces. Deep learning methods have recently achieved promising performance thanks to their powerful representation learning capacity. However, existing deep learning-based classifiers rely solely on tem...

Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information from knowledge graphs. Most existing explainable recommendations only utilize static knowledge graphs and ignore the dynamic user-item evolutions, leading to less convincing and inaccurate explanations. Although some w...

Fairness-aware recommendation eliminates discrimination issues to build trustworthy recommendation systems. Existing fairness-aware approaches ignore accounting for rich user and item attributes and thus cannot capture the impact of attributes on affecting recommendation fairness. These real-world attributes severely cause unfair recommendations by...

Gathering information from multi-perspective graphs is an essential issue for many applications especially for protein-ligand-binding affinity prediction. Most of traditional approaches obtained such information individually with low interpretability. In this paper, we harness the rich information from multi-perspective graphs with a general model,...

Recommender system helps address information overload problem and satisfy consumers’ personalized requirement in many applications such as e-commerce, social networks and in-game store. However, existing approaches mainly focus on improving the accuracy of recommendation tasks, but usually ignore to improve the interpretability of recommendation, w...

With the rapid development of emerging network technologies such as cloud computing, the background server-side of public health information services is widely deployed on the untrusted cloud, which has become one of the main threats of user health privacy leakage. To this end, this paper proposes an agent-based algorithm for the protection for use...

On-device recommender systems recently have garnered increasing attention due to their advantages of providing prompt response and securing privacy. To stay current with evolving user interests, cloud-based recommender systems are periodically updated with new interaction data. However, on-device models struggle to retrain themselves because of lim...

Session-based recommendation systems (SBRs) aim to capture user preferences over time by taking into account the sequential order of interactions within sessions. One promising approach within this domain is session graph-based recommendation, which leverages graph-based models to represent and analyze user sessions. However, current graph-based me...

E-commerce authoring involves creating attractive, abundant, and targeted promotional content to drive product sales. The emergence of large language models (LLMs) introduces an innovative paradigm, offering a unified solution to address various authoring tasks within this scenario. However, mainstream LLMs trained on general corpora with common se...

Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF) models. One classical approach in GCF is to learn user and item embeddings by modeling complex graph relations and utilizing these embeddings for CF models. However, the qual...

Fairness-aware recommendation eliminates discrimination issues to build trustworthy recommendation systems.Explaining the causes of unfair recommendations is critical, as it promotes fairness diagnostics, and thus secures users' trust in recommendation models. Existing fairness explanation methods suffer high computation burdens due to the large-sc...

Gathering information from multi-perspective graphs is an essential issue for many applications especially for proteinligand binding affinity prediction. Most of traditional approaches obtained such information individually with low interpretability. In this paper, we harness the rich information from multi-perspective graphs with a general model,...

Traditional music recommender systems are mainly based on users’ interactions, which limit their performance. Particularly, various kinds of content information, such as metadata and description can be used to improve music recommendation. However, it remains to be addressed how to fully incorporate the rich auxiliary/side information and effective...

Multivariate time series inherently involve missing values for various reasons, such as incomplete data entry, equipment malfunctions, and package loss in data transmission. Filling missing values is important for ensuring the performance of subsequent analysis tasks. Most existing methods for missing value imputation neglect inter-variable relatio...

As an indispensable personalized service in Location-based Social Networks (LBSNs), the next Point-of-Interest (POI) recommendation aims to help people discover attractive and interesting places. Currently, most POI recommenders are based on the conventional centralized paradigm that heavily relies on the cloud to train the recommendation models wi...

Loan default risk prediction is a major application of machine learning for financial institutions to evaluate the client's default probability. Existing deep learning models rarely consider the connection among application records for loan default detection. We believe similar records, as auxiliary information, are also significant for loan defaul...

Modern sociology has profoundly uncovered many convincing social criteria for behavioral analysis. Unfortunately, many of them are too subjective to be measured and very challenging to be presented in online social networks (OSNs). On the other hand, data mining techniques can better find data patterns but many of them leave behind unnatural unders...

People across the globe have felt and are still going through the impact of COVID-19. Some of them share their feelings and suffering online via different online social media networks such as Twitter. Due to strict restrictions to reduce the spread of the novel virus, many people are forced to stay at home, which significantly impacts people's ment...

In this paper, aiming at a Chinese keyword based book search service, from a technological perspective, we propose to modify a user query sequence carefully to confuse the user query topics and thus protect the user topic privacy on the untrusted server, without compromising the accuracy of each book search service. Firstly, we propose a client-bas...

Nowadays, many dynamic recommendations still suffer from the insufficiency of finding user online interest evolving patterns because of those complicated interactions. In general, each interaction is usually impacted by multiple underlying reasons, which needs us to open the “box” of each interaction instance instead of simply treating them as a pa...

The rapid advances in automation technologies, such as artificial intelligence (AI) and robotics, pose an increasing risk of automation for occupations, with a likely significant impact on the labour market. Recent social-economic studies suggest that nearly 50% of occupations are at high risk of being automated in the next decade. However, the lac...

The rapid advances in automation technologies, such as artificial intelligence (AI) and robotics, pose an increasing risk of automation for occupations, with a likely significant impact on the labour market. Recent social-economic studies suggest that nearly 50\% of occupations are at high risk of being automated in the next decade. However, the la...

Graph learning models are critical tools for researchers to explore graph-structured data. To train a capable graph learning model, a conventional method uses sufficient training data to train a graph model on a single device. However, it is prohibitive to do so in real-world scenarios due to privacy concerns. Federated learning provides a feasible...

Counterfactual explanations interpret the recommendation mechanism via exploring how minimal alterations on items or users affect the recommendation decisions. Existing counterfactual explainable approaches face huge search space and their explanations are either action-based (e.g., user click) or aspect-based (i.e., item description). We believe i...

Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation learning. The key to the success of graph contrastive learning is to acquire high-quality positive and negative samples as contrasting pairs for the purpose of learning underlying structural semantics of the input graph. Recent works usually sample negat...

In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. 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. Most of th...

The increasingly developed online platform generates a large amount of online reviews every moment, e.g., Yelp and Amazon. Consumers gradually develop the habit of reading previous reviews before making a decision of buying or choosing various products. Online reviews play an vital part in determining consumers’ purchase choices in e-commerce, yet...

A large amount of information exists in many e-commerce and review websites as a valuable source for recommender systems. Recent solutions focus on exploring the correlation between sentiment and textual reviews in the review-based recommendation. However, these studies usually pay less attention to the differences of different users in sentimental...

Reinforcement learning has recently become an active topic in recommender system research, where the logged data that records interactions between items and users feedback is used to discover the policy. Much off-policy learning, referring to the procedure of policy optimization with access only to logged feedback data, has been a popular research...

Although traditional recommendation methods trained on observational interaction information have engendered a significant impact in real-world applications, it is challenging to disentangle users' true interests from interaction data. Recent disentangled learning methods emphasize on untangling users' true interests from historical interaction rec...

The aim of this study is to analyse the coronavirus disease 2019 (COVID-19) outbreak in Bangladesh. This study investigates the impact of demographic variables on the spread of COVID-19 as well as tries to forecast the COVID-19 infected numbers. First of all, this study uses Fisher’s Exact test to investigate the association between the infected gr...

Deep neural networks currently achieve state-of-the-art performance in many multivariate time series classification (MTSC) tasks, which are crucial for various real-world applications. However, the black-box characteristic of deep learning models impedes humans from obtaining insights into the internal regulation and decisions made by classifiers....

Medication recommendation is a crucial task for intelligent healthcare systems. Previous studies mainly recommend medications with electronic health records(EHRs). However, some details of interactions between doctors and patients may be ignored in EHRs, which are essential for automatic medication recommendation. Therefore, we make the first attem...

Causal inference is capable of estimating the treatment effect (i.e., the causal effect of treatment on the outcome ) to benefit the decision making in various domains. One fundamental challenge in this research is that the treatment assignment bias in observational data. To increase the validity of observational studies on causal inference, repres...

Transformers have achieved state-of-the-art performance in learning graph representations. However, there are still some challenges when applying transformers to real-world scenarios due to the fact that deep transformers are hard to be trained from scratch and the memory consumption is large. To address the two challenges, we propose Graph Masked...

Recently, many pre-trained language models for source code have been proposed to model the context of code and serve as a basis for downstream code intelligence tasks such as code completion, code search, and code summarization. These models leverage masked pre-training and Transformer and have achieved promising results. However, currently there i...

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. Existing methods tracks this problem as eliminating bias for the robust recommendation, e.g., by re-weig...

The ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of...

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. Currently, several approaches have been proposed for binary-source code matching by jointly learning the embeddings of binary code and source code in a common vector...

Graph contrastive learning is the state-of-the-art unsupervised graph representation learning framework and has shown comparable performance with supervised approaches. However, evaluating whether the graph contrastive learning is robust to adversarial attacks is still an open problem because most existing graph adversarial attacks are supervised m...

Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain. Graph contrastive learning is one of the unsupervised graph representation learning methods, which recently attracts attention from researchers and has achieved state-of-the-art performance...

Claims analysis and risk management is key to avoiding fraud and managing risk in the life insurance industry. Though visualization plays a fundamental role in supporting analysis tasks in the business domain, exploring user behaviour remains a challenging task. The prevalence of natural language interactions enhanced with data visualization has be...

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. Existing methods track this problem as eliminating bias for the robust recommendation, e.g., by re-weigh...

Product reviews on e-commerce platforms play a critical role in shaping users’ purchasing decisions. Unfortunately, online reviews sometimes can be intentionally misleading to manipulate the ecosystem. To date, existing methods to automatically detect “spam reviews” either focus on sophisticated feature engineering with traditional classification m...

Recently, deep learning techniques have yielded immense success on recommender systems. However, one weakness of most deep methods is that, users/items mutual semantic relationships, which are latent in the user-item interactions, are not distilled out explicitly. Moreover, most methods have been primarily focused on representation learning in eucl...