DOCTORAL ABSTRACT

Context & Semantics in News & Web Search
Daan Odijk
University of Amsterdam
Amsterdam, The Netherlands
daan@odijk.meAbstract
This thesis presents research towards a core aim of information retrieval (IR): providing
users with easy access to information. Three research themes guide the research presented inthis thesis, contributing to three aspects of IR research: the domain in which an IR system
is used, the users interacting with the system, and the different access scenarios in whichthese users engage with an IR system. Central to these research 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 ina news story. Motivated by how historians select documents for close reading, we propose
novel methods for connecting collections using automatically extracted temporal references.
To illustrate how these algorithms can be used to automatically create connections between
collections, we introduce a novel search interface to explore and analyze the connected collections. The interface highlights different perspectives and requires little domain knowledge.
Based on how communication scientists study framing in news, we propose an automaticthematic content analysis approach.
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. Based on these findings we propose ways in which systems can reduce
struggling in search. To support searchers, we propose and evaluate algorithms that accurately predict the nature of future actions and their anticipated impact on search outcomes.
Our findings have implications for the design of search systems that help searchers struggle
less and succeed more.
In the third and final research theme, we consider a pro-active search scenario, specificallyin a live television setting. We propose algorithms that leverage contextual information toretrieve diverse related content for a leaned-back TV viewer. While watching television,
people increasingly consume additional content related to what they are watching. Two
methods to automatically retrieve content based on subtitles are introduced, one using entity
linking, and one that uses reinforcement learning to generate effective queries for finding

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related content. Both methods are highly efficient and are currently used in a live televisionsetting in near real time.
Each research chapter in this thesis provides insights and algorithms that help searchers
when using IR applications. For varying domains, users, and access scenarios, the researchpresented in this thesis improves the ease of access to information.
Supervisor:
Co-supervisor:
Available online at:

ACM SIGIR Forum

Prof. dr. M. de Rijke, University of Amsterdam
Dr. E.J. Meij, Yahoo Labs
http://daan.odijk.me/thesis

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Vol. 50 No. 1 June 2016