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.
- Information search
- Machine learning