Using Contextual Information to Assist Desktop Search
As the volume of personal data grows, it becomes increasingly difficult to rapidly find what we need on our desktop computers. Traditional content-based desktop search tools provide a potential solution by indexing file content, while lacking of contextual information about the user's activity. Context-enhanced search engines seem promising: they re-rank content-based results with the user's context. However, most of them are incomplete answers: they neither consider the factor of network application (especially, Web browser), nor take full advantage of the characteristics of user behavior and file metadata on desktop computers. To address those issues, we propose a concept of "document-window switch tree" to represent the dynamic relationship from the user's context. We further collect relevant information to re-rank traditional content search results, such as reading time, frequency of window switch, file content and metadata. Search results on ranking accuracy generated by our method significantly outperform the context-enhanced search tool based on temporal locality.
contextual information desktop search support vector machines user interaction