Query expansion using gaze-based feedback on the subdocument level

Georg Buscher; Andreas Dengel; Ludger van Elst

In: S.-H. Myaeng; D.W. Oard; F. Sebastiani; T.-S. Chua; M.-K. Leong (Hrsg.). Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM SIGIR Conference on Information Retrieval (SIGIR-08), July 20-24, Singapore, Singapore, Pages 387-394, ISBN 978-1-60558-164-4, ACM, New York, NY, USA, 2008.


We examine the effect of incorporating gaze-based attention feedback from the user on personalizing the search process. Employing eye tracking data, we keep track of document parts the user read in some way. We use this information on the subdocument level as implicit feedback for query expansion and reranking. We evaluated three different variants incorporating gaze data on the subdocument level and compared them against a baseline based on context on the document level. Our results show that considering reading behavior as feedback yields powerful improvements of the search result accuracy of ca. 32% in the general case. However, the extent of the improvements varies depending on the internal structure of the viewed documents and the type of the current information need.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence