Dr Lim Wern Han

Scholarly Teaching Fellow
School of IT

lim.wern.han@monash.edu
+603 5515 9662
Room 2-4-33
ORCID

Personal statement

Wern Han Lim (Ian) has been part of the school of information technology as a sessional staff since 2015, having been involved in units such as FIT1008 Introduction to Computer Science, FIT2004 Algorithms and Data Structures, FIT2014 Theory of Computations and FIT3152 Data Science. He has a passion for teaching and was the recipient of the Faculty of Information Technology Teaching Award in 2017: Citation for Outstanding Contribution to Student Learning. His enjoys breaking down complex algorithms/ data structures to the students while analysing the complexities; and was one of the challenge contributors for the 2016 UniCode competition organised in collaboration with INTI Subang. Besides that, he has been actively involved in the field of data science and big data -- by actively seeking certifications; conducting data science workshops; and mentoring student teams in competitions.

Ian had his PhD conferred recently in the area of information retrieval (IR), particularly on user-generated content (UGC) such as Reddit. His work focuses on the estimation of user expertise to infer the information quality of their generated content; reducing the need for content processing. The user profiling borrows from data science techniques; introducing novel new models and approaches for UGC platforms. The inferred information quality can then be used to -- (1) identify good tags for accurate content description or classification;(2) finding experts for community question-answering platforms; (3) organising user comments according to their contribution; and (4) filtering out unwanted low-quality content such as spam. His goal is to support various computer science researches by providing data that are dense with high information quality.

Academic degrees

  • Doctor of Philosophy in Computer Science, Monash University, 2018
  • Degree in Computer Science, Monash University, 2009

Professional affiliations

Member of International Professional Bodies

  • Association for Computing Machinery, Member

Research Interests

Ian has a strong research interest to bring order to the World Wide Web (WWW) today – estimating the reliability and information quality of user-generated content. He aims to enhance the information retrieval (IR) experience on the WWW, leveraging the Wisdom of the Crowd. To do so, he utilizes various data science techniques and analytics to produce powerful models that can profile WWW users accurately. To date, he has contributed towards:

  • Classification of WWW resources using user annotations/ tags
  • Identifying experts on community question-answering (CQA) platforms
  • Prediction of best answer in a CQA environment
  • Identifying high quality comments on Reddit and filtering out unwanted comments

As of now, he is supervising 2 final year students on projects that enhances existing WWW platforms such as Wikipedia through knowledge generated by Reddit users.

Research Projects

Title: Reddit Fortification: Enriching Wikipedia

Reddit as a content aggregation platform often play host to highly informational discussion sessions between its users. One of such subreddit is the r/todayilearned where users share their new discoveries and discuss further about it with other Reddit users. Thus, this project aims to extract valuable knowledge from these conversations to further enrich Wikipedia as the popular repository of knowledge on the WWW; overcoming the shortage in contributors on the platform

Modern day consumers tend to refer to the WWW to aid their purchase decisions due to many possible reasons – (1) having difficulty in having access to the product; (2) are uncertain in their exact requirements or needs; (3) looking to seek expert opinions; and (4) to reinforce their choice. This however is not ideal for the consumers due to user bias as well as endorsed reviewers. Thus, the consumers would now seek peer recommendations through platforms such as Reddit. This research predicts product recommendation for consumers according to a wide range of factors such as use cases, budget, time, requirements, key features and many more. Unlike many of the earlier approaches, this research exploits the community’s wisdom of the crowd to provide up-to-date recommendations while uses such information to better inform the consumers and manage their expectations. The findings of what consumers what and prioritise could then be extracted to develop better products or services in the future.

Education

Units taught

  • FIT1008 Introduction to Computer Science
  • FIT2004 Algorithms and Data Structures
  • FIT2014 Theory of Computations
  • FIT3152 Data Science

Local grants

Update Coming Soon

Current supervision

Suvashish Chakraborty
Reddit Fortification: Enriching Wikipedia
3 (final year)
Monash University Malaysia

Errystio Rizky Tendean
What Do I Buy? Trusted Recommendations on Reddit
3 (final year)
Monash University Malaysia

Local Award/Recognition/Exhibition/Stewardship

  • Faculty of Information Technology Teaching Award 2017 – Faculty of Information Technology, Monash University, 2017

Publication

Selected Conference Proceedings

  • Wern Han Lim, and Mark James Carman (2017). Annotator Expertise and Information Quality in Annotation-based Retrieval. In Proceedings of the 22nd Australasian Document Computing Symposium (ADCS 2017). ACM, New York, NY, USA, Article 7, 8 pages.
  • Wern Han Lim, Mark James Carman, and Sze-Meng Jojo Wong (2017). Estimating Relative User Expertise for Content Quality Prediction on Reddit. In Proceedings of the 28th ACM Conference on Hypertext and Social Media (HT '17). ACM, New York, NY, USA, 55-64.
  • Wern Han Lim, Mark James Carman, and Sze-Meng Jojo Wong (2016). Estimating Domain-Specific User Expertise for Answer Retrieval in Community Question-Answering Platforms. In Proceedings of the 21st Australasian Document Computing Symposium (ADCS '16). ACM, New York, NY, USA, 33-40.

Selected Journals

  • Wern Han Lim, and Saadat M. Alhashmi (2010). Joint Web-feature (JFEAT): A Novel Web Page Classification Framework. Communications of the IBIMA.