Context & semantics in news & web search

This thesis presents research towards providing users with easy access to information. Three research themes guide the research, contributing to three aspects of IR research: the domain in which an IR system is used, the users interacting with the system, and the access scenario in which these users engage with an IR system. Central to these themes is the aim to gain insights into the behavior of searchers and develop algorithms to support them in their quest, whether it is a researcher exploring or studying a large collection, a web searcher struggling to find something, or a television viewer searching for related content. The first research theme is motivated by the information seeking tasks of researchers exploring and studying large collections. To enable their search on a larger scale, we propose computational methods to connect collections and to infer the perspective offered in a news story. The second research theme is addressed in a mixed-methods study on how web searchers behave when they cannot find what they are looking for. Based on large-scale log analysis, crowd-sourced labeling, and predictive modeling, we show behavioral differences given task success and failure and propose ways in which systems can reduce struggling in search. In the third research theme, we consider a pro-active search scenario, specifically in a live television setting. We propose algorithms that leverage contextual information to retrieve related content for a leaned-back TV viewer that are highly efficient and are currently used in a live television setting in near real time.