DOCTORAL ABSTRACT

Temporal Dynamics in Information Retrieval
Stewart Whiting
School of Computing Science,
University of Glasgow,
Scotland, UK.
stewh@dcs.gla.ac.uk
Abstract
Since time is an omnipresent feature of our existence, many elements of time are embedded
in information itself, and related behaviours such as creation, seeking and utilisation. InIR, time can distinguish the interpretation of information, and influence the intentions andexpectations of users’ information seeking activity. Many time-based patterns and trends namely temporal dynamics - are evident in streams of information behaviour by individuals
and crowds. A temporal dynamic refers to a periodic regularity, or, a one-off or irregular
past, present or future of a particular element (e.g., word, topic or query popularity) - driven
by predictable and unpredictable time-based events and phenomena.
Several challenges and opportunities related to temporal dynamics emerge in IR. This
thesis explores temporal dynamics from the perspective of (i) query popularity and meaning,and (ii) word use and relationships over time. In particular, I consider how real-time temporal
dynamics in information seeking should be supported for consistent user satisfaction overtime, and moreover, how previously observed temporal dynamics offer a complementary
dimension which can be exploited to inform more effective IR systems.
Uncertainty about user expectations is a perennial problem for IR systems, further confounded by changes over time. Addressing this, IR systems can either assist the user tosubmit an effective query (e.g., error-free and descriptive), or better anticipate what the user
is most likely to want in relevance ranking. I first explore methods to always help users
formulate queries with time-aware query auto-completion capable of suggesting both recent
and always popular queries. I propose and evaluate several novel approaches, and demonstrate state-of-the-art performance of up to +9.2% improvement above existing baselines fordiverse search scenarios in different languages. Furthermore, I explore the impact of temporal dynamics on the motives behind users’ information seeking, and thus how relevanceitself is subject to temporal dynamics. I find the most likely meaning of ambiguous queries isaffected over short and long-term periods (e.g., hours to months) by several periodic and oneoff event-driven temporal dynamics. Finally, I find that for many event-driven multi-facetedqueries, relevance can often be inferred by modelling the temporal dynamics of changes inrelated information.
IR approaches are typically based on methods which characterize the nature of information through the statistical distributions of words and phrases. I model and exploit thetemporal dimension of the collection, captured by temporal dynamics, in these established IRACM SIGIR Forum97Vol. 50 No. 1 June 2016

approaches. I explore how the temporal dynamic similarity of word and phrase use in a collection can be exploited to infer temporal semantic relationships between the terms. I propose
an approach to uncover a query topic’s “chronotype” terms - that is, its most distinctive andtemporally interdependent terms, based on a mix of temporal and non-temporal evidence.
Experiments demonstrate that exploiting chronotype terms in temporal query expansion
leads to significantly improved retrieval performance in several time-based collections.
Temporal dynamics provide both a challenge and an opportunity for IR systems. Overall,this thesis demonstrates that temporal dynamics can be used to derive tacit structure andmeaning of information and information behaviour, which is valuable for improving timeaware IR system effectiveness.
Available from: http://theses.gla.ac.uk/6850/

ACM SIGIR Forum98

Vol. 50 No. 1 June 2016