Personal details

Associate Professor Yue Xu
Associate Professor
Faculty of Science,
School of Computer Science
Discipline *
Artificial Intelligence and Image Processing, Information Systems
+61 7 3138 1975
View location details (QUT staff and student access only)
Identifiers and profiles

Doctor of Philosophy (University of New England)


Data mining, Information retrieval, Recommender systems, Web intelligence

* Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2008


Associate Prof. Yue Xu has been an active researcher in the areas of web intelligence and data mining since she obtained her PhD in 2000. She has published over 140 refereed papers. Her current research interests are focused on recommender systems, text mining, pattern and association mining, and user interest and behavior modeling.

  • — Personalized Web based Recommender Systems

Web users are often overwhelmed by the huge amount of information on the Web and are faced with the great challenge of finding the most relevant information. Recommender systems are one of the tools designed to help users deal with the information explosion by giving information recommendations.
Current topics in this area include:

  • The development of user profiling models (especially multi-dimensional user profiling) to discover and represent users’ information interest based on user generated content such as product ratings, tagging data, blogs, reviews, etc.
  • The development of context-aware recommendation models by taking contextual information into consideration such as time, location, weather, companion, etc.
  • The development of product reputation models and integrating product reputation into recommendation making
  • The development of distributed recommendation algorithms to tackle the scalability problem faced by recommender systems in massive multi-dimensional data environment
  • — Pattern and association mining

In order to help people understand and utilize massive amount of data, pattern and association mining provide techniques to automatically discover interesting patterns or associations between data items from large datasets. One big concern with the quality of association rule mining is the huge amount of discovered rules among which many are redundant thus useless in practice. Current topics in this area include:

  • The development of techniques to generate compact representations of patterns and association rules in order to enhance the smart use of the discovered knowledge in practice
  • The development of pattern mining techniques on large-scale heterogeneous datasets to discover cross-discipline patterns and associations
  • The development of pattern based topic modelling by incorporating pattern mining techniques with statistical topic modelling to build more accurate topic models to represent a given dataset
  • —Text mining for understanding large text corpus

With the rising and developing of Web2.0 websites, increasing amount of textual data becomes available and people need techniques to understand large text corpus quickly and accurately. Topic modeling is a useful technique to discover the theme from a collection of texts. This research is to develop new methods to evaluate the quality and interpret the semantic meaning of the discovered topics in order to correctly understand the corpus. Another focus in this area is to analyze online product reviews in order to find customers’ opinions towards products. Nowadays, large number of websites encourage visitors to post reviews regarding products or services. These reviews are useful for both information publishers and readers. This sort of feedback could be used to evaluate customer satisfaction about products or services and take appropriate and sensible actions based on this information. This research is to develop text mining techniques to extract customer opinions from customer reviews.

  • —Modeling User Behavior on Social Networks for Detection of Social Engineering Attacks

Online social networks such as Facebook and Twitter have experienced a huge surge in popularity. The distinctive features make OSNs not only attractive to faithful users but also malicious users or attackers. Deceiving, persuading, or influencing people to provide critical information or perform an action that will benefit the attacker is known as Social Engineering. The aim of this research project is to develop data mining methods to generate user online behavior models by mining user generated online data and discover the associations between user behavior features and the source characteristics that influence users’ susceptibility to social engineering victimization.
Current Research Students (Principal supervisor):

  • Joseph Gear, PhD candidate, Research topic: Automated Vulnerability Discovery for Industrial Control Systems using Machine Learning
  • Seajung Im, PhD candidate, Research topic: Hospital readmission prediction model based on healthcare and social media data
  • Yashodhya Vachila Wijesinghe, PhD candidate, Research topic: Knowledge-based Intelligenct System for automatic prediction of fall and frailty of seniors
  • Dakshi Geeganage, PhD candidate, Research topic: Concept Embedded Topic Modeling Technique
  • Darshika Koggalahewa, PhD candidate, Research topic: Domain Ontology learning for product recommendations based on domain information sources
  • Puspa Setia Pratiwi, PhD candidate, Research topic:  System Architecture for Enabling Continuous Automated Data-driven Personalisation of Wellness Coaching
  • Tran Diem Hanh Nguyen, PhD candidate, Research topic: Context-aware Recommender Systems

Completion (Principal supervisor):

  • Dr. Vin San Chia, Professional Doctorate of IT (2020), Thesis:  New Metrics for Assessing High-quality Researchers
  • Dr. Bandar Alghamdi, PhD(2020), Thesis: Topic-based Feature Selection and A Hybrid Approach for Detecting Spammers on Twitter
  • Anh Duc Nguyen (Dion), Master by Research (2018), Thesis: Review selection based on Topic models
  • Dr. Nan Tian, PhD(2016), Thesis: Feature Taxonomy Learning from User Generated Content and Application in Review Selection
  • Dr. Xiaoyu (Tyler) Tang, PhD (2016), Thesis: Recommendation Framework based on the Incorporation of Nearest Neighbourhood and Tensor Factorization
  • Dr. Abdullah Ayed Algarni, PhD (2016), Thesis:  The impact of Source characteristics on users’ susceptibility to social engineering victimization in social networks
  • Dr. Ahmad Abdel-Hafez, PhD (2016). Thesis: Reputation Model Based On Rating Data and Application in Recommender Systems
  • Dr. Yang (Grace) Gao, PhD (2015) . Thesis: Pattern-based Topic Modelling and its Application for Information Filtering
  • Dr. Nora Abdullah, PhD (2012). Thesis: Integrating Collaborative Filtering and Matching-based Search for Recommending Infrequently Purchased Products
  • Dr. Touhid Bhuiyan, PhD (2011). Thesis: Trust-based Automated Recommendation Making
  •  Dr. Huizhi(Elly) Liang, PhD (2011). Thesis: User Profiling Based on Folksonomy Information in Web 2.0 for Personalized Recommender Systems
  • Dr. Gavin Shaw, PhD (2010). Thesis: Discovery and Effective Use of Quality Association Rules in Multi-Level Datasets
  • Dr. Li-Tung (Soloman) Weng, PhD (2008). Thesis: Information Enrichment for Quality Recommender Systems
  • Mr. Hao (Harry) Zang, Master by research (2010). Thesis: Non-redundant Sequential Association Rule Mining based on Closed Sequential Patterns
  • Mr. Zhihan (John) Li, Master by research (2009). Thesis: Improvement to Chinese Information Retrieval by Incorporating Word Segmentation and Text Mining Techniques
This information has been contributed by Associate Professor Yue Xu.


Teaching discipline: Computer Science   

  • Data warehousing and mining
  • Building IT systems
  • Data structure and algorithms
  • Agile software development
  • Programming principles
  • Artificial intelligence


This information has been contributed by Associate Professor Yue Xu.


For more publications by this staff member, visit QUT ePrints, the University's research repository.


Awards and recognition

Academic Honours, Prestigious Awards or Prizes
Reference year
Best Paper Award, Product Feature Taxonomy Learning based on User Reviews, Nan Tian, Yue Xu, Yuefeng Li, Ahmad Abdel-Hafez and Audun Josang, the 10th International Conference on Web Information Systems and Technologies (WEBIST 2014), 3-5 April 2014, Barcelona, Spain.
Academic Honours, Prestigious Awards or Prizes
Reference year
Best Paper Award, Personalization in tag ontology learning for recommendation making, E. Djuana, Y. Xu, Y. Li, C. Cox, the14th International Conference on Information Integration and Web-based Applications & Services (iiWAS2012), Bali, Indonesia, 2012.
Academic Honours, Prestigious Awards or Prizes
Reference year
Best Paper Award, Web information recommendation making based on item taxonomy, Soloman Weng, Yue Xu, Yuefeng Li and Richi Nayak, the 10th International Conference on Enterprise Information Systems(ICEIS08), June 2008, Barcelona, Spain, 2008.
Academic Honours, Prestigious Awards or Prizes
Reference year
Outstanding Doctoral Thesis, Knowledge discovery using pattern taxonomy model in text mining, Sheng-Tang Wu (Supervisors: Yuefeng Li and Yue Xu), 2007.

Research projects

Grants and projects (Category 1: Australian Competitive Grants only)

Web Services Reputation Management
Primary fund type
CAT 1 - Australian Competitive Grant
Project ID
Start year
Web Services;Reputation;Trust Management;Security;Quality of Service;Service Oriented Architecture