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Attention-based Information Retrieval Using Eye Tracker Data

Miller, Tristan and Agne, Stefan (2005):
Attention-based Information Retrieval Using Eye Tracker Data.
In: Proceedings of the Third International Conference on Knowledge Capture (K-CAP05), DOI: 10.1145/1088622.1088672,
[Online-Edition: https://dx.doi.org/10.1145/1088622.1088672],
[Conference or Workshop Item]

Abstract

We describe eFISK, an automated keyword extraction system which unobtrusively measures the user's attention in order to isolate and identify those areas of a written document the reader finds of greatest interest. Attention is measured by use of eye-tracking hardware consisting of a desk-mounted infrared camera which records various data about the user's eye. The keywords thus identified are subsequently used in the back end of an information retrieval system to help the user find other documents which contain information of interest to him. Unlike traditional IR techniques which compare documents simply on the basis of common terms withal, our system also accounts for the weights users implicitly attach to certain words or sections of the source document. We describe a task-based user study which compares the utility of standard relevance feedback techniques to the keywords and keyphrases discovered by our system in finding other relevant documents from a corpus.

Item Type: Conference or Workshop Item
Erschienen: 2005
Creators: Miller, Tristan and Agne, Stefan
Title: Attention-based Information Retrieval Using Eye Tracker Data
Language: English
Abstract:

We describe eFISK, an automated keyword extraction system which unobtrusively measures the user's attention in order to isolate and identify those areas of a written document the reader finds of greatest interest. Attention is measured by use of eye-tracking hardware consisting of a desk-mounted infrared camera which records various data about the user's eye. The keywords thus identified are subsequently used in the back end of an information retrieval system to help the user find other documents which contain information of interest to him. Unlike traditional IR techniques which compare documents simply on the basis of common terms withal, our system also accounts for the weights users implicitly attach to certain words or sections of the source document. We describe a task-based user study which compares the utility of standard relevance feedback techniques to the keywords and keyphrases discovered by our system in finding other relevant documents from a corpus.

Title of Book: Proceedings of the Third International Conference on Knowledge Capture (K-CAP05)
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 31 Dec 2016 14:29
DOI: 10.1145/1088622.1088672
Official URL: https://dx.doi.org/10.1145/1088622.1088672
Identification Number: TUD-CS-2005-0040
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