Paper Info
Title
An Eye-Tracking Study of Query Reformulation
Abstract
Information about a user's domain knowledge and interest can be important signals for many information retrieval tasks such as query suggestion or result ranking. State-of-the-art user models rely on coarse-grained representations of the user's previous knowledge about a topic or domain. In this paper, we study query refinement using eye-tracking in order to gain precise and detailed insight into which terms the user was exposed to in a search session and which ones they showed a particular interest in. We measure fixations on the term level, allowing for a detailed model of user attention. To allow for a wide-spread exploitation of our findings, we generalize from the restrictive eye-gaze tracking to using more accessible signals: mouse cursor traces. Based on the public API of a popular search engine, we demonstrate how query suggestion candidates can be ranked according to traces of user attention and interest, resulting in significantly better performance than achieved by an attention-oblivious industry solution. Our experiments suggest that modelling term-level user attention can be achieved with great reliability and holds significant potential for supporting a range of traditional IR tasks.
Year
DOI
Venue
2015
10.1145/2766462.2767703
International Conference on Research an Development in Information Retrieval
Keywords
Field
DocType
Eye-gaze Tracking,Knowledge Acquisition,Domain Expertise,Query Reformulation,Query Refinement,Query Suggestion,Mouse Cursor Tracking
Query optimization,Web search query,Data mining,Query language,Information retrieval,Query expansion,Computer science,Sargable,Web query classification,Ranking (information retrieval),Online aggregation
Conference
ISBN
Citations 
PageRank 
978-1-4503-3621-5
15
0.71
References 
Authors
39
3
Name
Order
Citations
PageRank
Carsten Eickhoff136539.21
Sebastian Dungs2244.52
Vu Tran3181.09