There is a growing diversity of information access applications. While general web search has been dominant in the past few
decades, a wide variety of so-called vertical search tasks and applications have come to the fore. Vertical search is an often
used term for search that targets specific content. Examples include YouTube video search, Facebook graph search, Spotify
music recommendation, product search, expertise retrieval, and scientific literature search.
In a vertical search application,
typically, some background knowledge is available about the context in which search is taking place. We may know something
about the user population, about the tasks they wish to perform, about their information needs, and about the information
objects in the collection we make available to them. This knowledge can inform adaptation of retrieval algorithms and evaluation
methodology, to provide a better ranking of information objects, or to organize search results more effectively.
dissertation showcases the need, as well as many opportunities, to leverage background knowledge in three vertical search
scenarios: finding people, finding scientific papers, and finding microblog posts. Its five research chapters provide pointers
on how background knowledge may be used to help understand user information needs, organize search results, evaluate retrieval
algorithms, and automatically generate ground truth.