Learning search pattern for construction procurement using keyword net

Abstract

As more and more procurement websites become available on the Internet, seeking information from websites has become an essential part of a contractor's procurement undertaking. Several e-markets, specifically for construction, have also been established, including bLiquid.com and ProcureZone. However, most websites provide only two primary ways of searching for information, namely by index/menu or by keyword. Instead of relying on the primitive search engines found in most procurement websites, a search guide system could help a user's keyword search by reducing the number of keywords required to find the desired information. Our research recognized that professional procurement experience helped users more effectively carry out website information searches, by using fewer keywords. We planned to capture such experience in order to guide inexperienced users in their search. The research goal was to improve search effectiveness by guiding the user's search using three approaches, namely correction, specification and extension. Based on these three approaches, this research applied the following guides: correction; specification-by-equivalence; specification-by-detail; extension-by-time; extension-by-location; extension-by-team; and extension-by-component. The paper will describe how we classified users for learning credibility, and the learning framework for recording expert users' search patterns. Twelve professionals, using 14 procurement packages, with 64 items in total, evaluated the proposed framework. It will be demonstrated that the proposed learning keyword guide facilitated a dynamic, customized menu and indexing system, and reduced the number of keywords required for the professionals to find the information they desired.