Using relevance feedback to improve personal web page search precision

• Main Authors: Wen-Xian Lai, 賴文賢
• Other Authors: Shi-Hao Liu
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/33625959038510439733

碩士 === 中原大學 === 資訊管理研究所 === 103 === In recent years, because internet penetration rate is raised and the development of Web 2.0 in Taiwan have been stable, there are more and more personal users taking part in and discussing the internet issues, internet public opinion firmed in this way. For celebrity, the internet opinion will affect their image and support, no matter positive or negative. When celebrity searched the latest internet opinion about themselves by search engine, it couldn’t quickly sort the latest issues about themselves to top. In addition to facing the age of the Big data, if searched accuracy is lower, personal users will be more inefficient. Therefore, the research purpose is building a personal monitoring system of public opinion on internet. It is according to the user’s relevance feedback and daily learning mechanism of keyword weighting by the study. Both of them are the system’s basic of learning and distinguishing the latest related opinion. According to past three day’s result of keyword weighting, it can predict next day’s relevant sequence of searching result in order to improve next day’s personal web page search precision. Search results show that this personal monitoring system of public opinion on internet can improve personal web page search precision. But each day’s search precision is inversely proportional to each day’s total number of learning issues.