DOCTORAL ABSTRACT

Implicit Feedback for Interactive Information Retrieval
Ryen W. White
University of Glasgow
ryen@umiacs.umd.edu
Supervisors : Joemon M. Jose, Ian Ruthven and C. J. van Rijsbergen
Thesis is available online at http://umiacs.umd.edu/~ryen
Searchers can find the construction of query statements for submission to Information Retrieval (IR)systems a problematic activity. These problems are confounded by uncertainty about the information
they are searching for, or an unfamiliarity with the retrieval system being used or collection being
searched. On the World Wide Web these problems are potentially more acute as searchers receive littleor no training in how to search effectively. Relevance feedback (RF) techniques allow searchers todirectly communicate what information is relevant and help them construct improved query statements.
However, the techniques require explicit relevance assessments that intrude on searchers’ primary lines ofactivity and as such, searchers may be unwilling to provide this feedback. Implicit feedback systems areunobtrusive and make inferences of what is relevant based on searcher interaction. They gather
information to better represent searcher needs whilst minimising the burden of explicitly reformulating
queries or directly providing relevance information.
This thesis investigates implicit feedback techniques for interactive information retrieval. The techniques
proposed aim to increase the quality and quantity of searcher interaction and use this interaction to infersearcher interests. Search interfaces are developed that use representations of the top-ranked retrieved
documents such as sentences and summaries to encourage a deeper examination of search results anddrive the information seeking process.
Implicit feedback frameworks based on heuristic and probabilistic approaches are described. Theseframeworks use interaction to identify needs and estimate changes in these needs during a search. The
evidence gathered is used to modify search queries and make new search decisions such as re-searching
the document collection or restructuring already retrieved information. The term selection models fromthe frameworks and elsewhere are evaluated using a simulation-based evaluation methodology that allows
different search scenarios to be modelled. Findings show that the probabilistic term selection model
generated the most effective search queries and learned what was relevant in the shortest time.
Different versions of an interface that implements the probabilistic framework are evaluated to test it withhuman subjects and investigate how implicit feedback is best implemented at the interface. The results
show that searchers are happy to delegate responsibility to RF systems for relevance assessment (throughimplicit feedback), but not more severe search decisions such as formulating queries or selecting retrieval
strategies. Systems that help searchers make these decisions are preferred to those that act directly on
their behalf or await searcher action.
Current Affiliation: Institute of Advanced Computer Studies, University of Maryland at College Park,College Park, MD USA 20742

ACM SIGIR Forum70

Vol. 39 No. 1 June 2005