Assoc Prof How S C

Assoc. Prof. Ts. Dr. Haw Su Cheng

Associate Professor

Assoc. Prof. Ts. Dr. Haw Su Cheng is Associate Professor at Faculty Of Computing and Informatics (FCI)


Associate Professor


Faculty Of Computing and Informatics (FCI)



Academic Qualifications

  • Ph.D., Multimedia University.
  • Master of Science (I.T.), University Putra Malaysia (UPM).
  • Bachelor Science (Hons) in Physics and minoring in Computer Science, University Putra Malaysia.

Research Interests

XML Databases, Recommender Systems, Data Warehouse, Semantic Web and Ontology.

Latest Research

i-Recommeder: Intelligent Recommender System based on Interaction Analytics

The i-Recommender, is an intelligent system that allows users to not only access the product-recommendations, but also the dashboard systems. The i-Recommender contains three main modules which are recommendation system module, descriptive analytics module and predictive analytics module. The recommendation system module aims to provide personalized recommendation to shopper to visit the merchant at a particular retailer, which the shopper will most likely wish to purchase/subscribe the items/services. In addition, i-Recommender is able to provide an unified approach to visualize the interactions level of data, i.e., filter out the unnecessary, and provides the most relevant processed data for decision making purpose. The final deliverable of this project is software component (API-based) that can easily integrated to any online retailer platform.

Funded Research

Ongoing (2019 - 2021)

  • Development of Automated Web Crawling and Indexing to Enhance MyCite Citation System using Graph Database

Completed (2019 - 2021)

  • i-Recommender: Intelligent Recommender System based on Interaction Analytics

Completed (2015 - 2018)

  • HyppTV Recommender Engine
  • Towards Optimizing Query Retrieval in Distributed XML Databases: Prunning and Federation
  • Web Entity Representation Using Structured Domain Knowledge And Social Contexts

Professor Postgraduate Students’ Paths

Under my supervision, I have 13 postgraduate students graduated (PhD and Master) and 6 active postgraduate students (by research).   I have also taught several postgraduate students by coursework. To me, every student is unique and every student has their own strengths. Thus, I treat all my students uniquely.  Most importantly, students need to aim and scope their proposal appropriately based on the degree level to ensure they are able to graduate on time.  Last but not least, passion is the keyword to success.