Learning search keywords for construction procurement

Ren-Jye Dzeng*, Shih Yu Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Seeking information from websites has become an essential part of a contractor's procurement undertaking, as more and more procurement websites become available on the Internet. Websites host extremely large amounts of information; a keyword search, therefore, is often more efficient than browsing via an index. However, in order to find the desired information, it may be necessary to enter keywords using a trial-and-error process. This research recognizes that professional procurement experience can help users search website information more effectively, by using fewer keywords, and so proposes a learning model and suggestion model that can capture such experience, thus guiding inexperienced users in their search. Experiments, evaluating the performance of the system, were also conducted.

Original languageEnglish
Pages (from-to)45-58
Number of pages14
JournalAutomation in Construction
Issue number1
StatePublished - 1 Jan 2005


  • E-commerce
  • Information search
  • Machine learning
  • Procurement

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