Enhancing web search user experience : from document retrieval to task recommendation

The World Wide Web is the biggest and most heterogeneous database that humans have ever built, making it the place of choice where people search for any sort of information through Web search engines. Indeed, users are increasingly asking Web search engines for performing their daily tasks (e.g., "planning holidays", "obtaining a visa", "organizing a birthday party", etc.), instead of simply looking for Web pages. In this Ph.D. dissertation, we sketch and address two core research challenges that we claim next-generation Web search engines should tackle for enhancing user search experience, i.e., Web task discovery and Web task recommendation. Both these challenges rely on the actual understanding of user search behaviors, which can be achieved by mining knowledge from query logs. Search processes of many users are analyzed at a higher level of abstraction, namely from a "task-by-task" instead of a "query-by-query" perspective, thereby producing a model of user search tasks, which in turn can be used to support people during their daily "Web lives".