Enhancing personalized search by mining and modeling task behavior

Personalized search systems tailor search results to the current user intent using historic search interactions. This relies on being able to find pertinent information in that user's search history, which can be challenging for unseen queries and for new search scenarios. Building richer models of users' current and historic search tasks can help improve the likelihood of finding relevant content and enhance the relevance and coverage of personalization methods. The task-based approach can be applied to the current user's search history, or as we focus on here, all users' search histories as so-called "groupization" (a variant of personalization whereby other users' profiles can be used to personalize the search experience). We describe a method whereby we mine historic search-engine logs to find other users performing similar tasks to the current user and leverage their on-task behavior to identify Web pages to promote in the current ranking. We investigate the effectiveness of this approach versus query-based matching and finding related historic activity from the current user (i.e., group versus individual). As part of our studies we also explore the use of the on-task behavior of particular user cohorts, such as people who are expert in the topic currently being searched, rather than all other users. Our approach yields promising gains in retrieval performance, and has direct implications for improving personalization in search systems.