Modeling facial expressions and peripheral physiological signals to predict topical relevance

Arapakis, I., Konstas, I., Jose, J. and Kompatsiaris, I. (2009) Modeling facial expressions and peripheral physiological signals to predict topical relevance. In: 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Boston, MA, USA, 19-23 Jul 2009, pp. 728-729. ISBN 9781605584836 (doi: 10.1145/1571941.1572099)

Abstract

By analyzing explicit and implicit feedback information retrieval systems can determine topical relevance and tailor search criteria to the user's needs. In this paper we investigate whether it is possible to infer what is relevant by observing user affective behaviour. The sensory data employed range between facial expressions and peripheral physiological signals. We extract a set of features from the signals and analyze the data using classification methods, such as SVM and KNN. The results of our initial evaluation indicate that prediction of relevance is possible, to a certain extent, and implicit feedback models can benefit from taking into account user affective behavior.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Jose, Professor Joemon and Konstas, Mr. Ioannis and Arapakis, Mr Ioannis
• Authors: Arapakis, I., Konstas, I., Jose, J., and Kompatsiaris, I.
• Subjects: Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science; Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9781605584836