Context-aware Search for Personal Information Management Systems - Chen, Jidong; Wu, Wentao; Guo, Hang; Wang, Wei (Proceedings of the Twelfth SIAM International Conference on Data Mining (SDM 2012), 2012)
With the fast growth of disk capacity in personal computers, keyword search over personal data (a.k.a. desktop search) is becoming increasingly important. Nonetheless, desktop search has been shown to be more challenging than traditional Web search. Modern commercial Web search engines heavily rely on structural information (i.e., hyperlinks between Web pages) to rank their search results. However, such information is not available in the circumstance of desktop search. Therefore, state-of-the-art desktop search systems such as Google Desktop Search usually leverage pure text-based ranking approaches (e.g., TF-IDF), which often fail to give promising rankings due to the misinterpretation of user intention. We observed that in desktop search, the semantics of keyword queries are often context-aware, i.e., they are related to the current activity state (e.g., writing a paper, navigating a website, etc.) of the user. In this paper, we present a novel context-aware search framework by taking this activity information into consideration. Specifically, we use Hidden Markov Model (HMM) to capture the relationships between user's access actions (e.g., opening/closing files, sending/receiving emails, etc.) and activity states. The model is learned from user's past access history and is used to predict user's current activity upon the submission of some keyword query. We further propose a ranking scheme with this predicted context information incorporated. Experimental evaluation demonstrates both the effectiveness of the proposed context-aware search method and the enhancement to user's search experience.