Adaptive Referencability Estimation for Healthcare Web Pages

網際網路已成為民眾獲取健康資訊的重要來源之ㄧ。
然而在醫療領域中，資訊之可參考度特別重要，包括
資訊之完整性、可靠度、合適之深淺度、語言、及領
域等。據此，本文提出一個資訊技術MyRR，自動依
據使用者指定之高參考度網頁，將搜尋引擎回傳結果
進行可參考度之重新排序，以方便使用者閱讀符合自
己需求且具高參考價值之資訊。實驗證實，MyRR 可
大幅提升醫學資訊網頁之可參考度辨識效能。此外，
MyRR 之計算有別以往，不需蒐集整個全球資訊網和
費時之迭代運算，在日新月異之潮流中，更能即時因
應使用者之需求。
The Internet has been an important healthcare
information source for general users and healthcare
professionals. Information referencability is particularly
essential in healthcare. It considers integrity, reliability,
skill levels, languages, and domains of healthcare
information. Therefore, we propose a technology MyRR,
which intelligently re-ranks healthcare web pages based
on their referencability, which is adaptively estimated
based on a set of web pages that deserves reference by
individual users. Users may thus read good information
to fit their needs. Experimental results show that MyRR
can significantly improve the performance of
referencability estimation. Moreover, MyRR achieves the
estimation without conducting a time-consuming crawl on
the entire WWW.