Personalizing entity detection and recommendation with a fusion of web log mining techniques

Given the proliferation of technology sites and the growing diversity of their readership, readers are more and more likely to encounter specialized language and terminology that they may lack the sufficient background to understand. Such sites may lose readership and the experience of readers may be impacted negatively if readers cannot quickly and easily find information about terms they wish to learn more about. We developed a system using a fusion of web log mining techniques that extracts, identifies, and recommends personalized terms to readers by utilizing information found in individual and global web query logs. In addition, the system presents relevant information related to these terms inline with the text. Our system outperforms some other related systems developed in the literature with special regard to usability.