Leveraging User Interaction to Improve Search Experience with Difficult and Exploratory Queries by Alexander

The query-based search paradigm is based on the assumption that the searchers are able to come up with the effective differentiator terms to make their queries specific and precise. In reality, however, a large number of queries are problematic return either too many or no relevant documents in the initial search results. Existing search systems provide no assistance to the users when they cannot formulate an effective keyword query and receive the search results of poor quality. In some cases, the users may intentionally formulate broad or exploratory queries (for example, when they want to explore a particular topic without having a clear search goal). In other cases, the users may not know the domain of the search problem sufficiently well and their queries may suffer from the problems, of which they may not be aware, such as ambiguity or vocabulary mismatch. Although the quality of search results can be improved by reformulating the queries, finding a good reformulation is often non-trivial and takes time. Therefore, in addition to the existing work on using the relevant documents from the top-ranked initially retrieved results to retrieve more relevant documents, it is important from both theoretical and practical points of view to also develop an interactive retrieval model, which would allow the search systems to improve the users’ search experience with exploratory queries, which return too many relevant documents, and difficult queries, which return no relevant documents in the initial search results. In this thesis, we propose and study three methods for interactive feedback that allow the search systems to interactively improve the quality of retrieval results for difficult and exploratory queries: question feedback, sense feedback and concept feedback. All three methods are based on a novel question-guided interactive retrieval model, in which a search system collaborates with the users in achieving their search goals by generating the natural language refinement questions. The first method, question feedback is aimed at interactive refinement of short, exploratory keywordbased queries by automatically generating a list clarification questions, which can be presented next to the standard ranked list of the retrieved documents. Clarification questions place the broad query terms into a specific context and help the user focus on and explore a particular aspect of the query topic. By clicking on a question, the users are presented with an answer to it and by clicking on the answer they can be redirected to the document containing the answer for further exploration. Therefore, clarification questions