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  1. Gwizdka, J., & Zhang, Y. (2015). Differences in eye-tracking measures between visits / revisits to relevant and irrelevant Web pages. Short paper and poster at SIGIR 2015.
  2. Gwizdka, J., & Zhang, Y. (2015). Towards Inferring Web Page Relevance – An Eye-Tracking Study. Poster presented at iConference'2015. Retrieved from


  1. Eugster, M. J. A., Ruotsalo, T., Spapé, M. M., Kosunen, I., Barral, O., Ravaja, N., … Kaski, S. (2014). Predicting Term-relevance from Brain Signals. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 425–434). New York, NY, USA: ACM.
  2. Gwizdka, J. (2014a). Characterizing Relevance with Eye-tracking Measures. In Proceedings of the 5th Information Interaction in Context Symposium (pp. 58–67). New York, NY, USA: ACM.
  3. Gwizdka, J. (2014b). News Stories Relevance Effects on Eye-movements. In Proceedings of the Symposium on Eye Tracking Research and Applications (pp. 283–286). New York, NY, USA: ACM.
  4. Ševcech, J., & Bieliková, M. (2014). User’s Interest Detection through Eye Tracking for Related Documents Retrieval. In 2014 9th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) (pp. 9–13).
  5. Zhang, Y. , Gwizdka, J. (2014). Effects of Tasks at Similar and Different Complexity Level . Poster presented at ASIST 2014.


  1. Frey, A., Ionescu, G., Lemaire, B., Lopez-Orozco, F., Baccino, T., & Guerin-Dugue, A. (2013). Decision-making in information seeking on texts: an Eye-Fixation-Related Potentials investigation. Frontiers in Systems Neuroscience, 7(39).
  2. Gwizdka J. (2013). Looking for Information Relevance In the Brain. Paper presented at NeuroIS 2013. BEST PAPER
  3. Moshfeghi, Y., & Jose, J. M. (2013a). An effective implicit relevance feedback technique using affective, physiological and behavioural features. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (pp. 133–142). New York, NY, USA: ACM.
  4. Moshfeghi, Y., & Jose, J. M. (2013b). On Cognition, Emotion, and Interaction Aspects of Search Tasks with Different Search Intentions. In Proceedings of the 22Nd International Conference on World Wide Web (pp. 931–942). Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee. Retrieved from
  5. Moshfeghi, Y., Pinto, L. R., Pollick, F. E., & Jose, J. M. (2013). Understanding Relevance: An fMRI Study. In P. Serdyukov, P. Braslavski, S. O. Kuznetsov, J. Kamps, S. Rüger, E. Agichtein, … E. Yilmaz (Eds.), Advances in Information Retrieval (pp. 14–25). Springer Berlin Heidelberg. Retrieved from  BEST PAPER
  6. Ajanki, A. (2013). Inference of relevance for proactive information retrieval. Retrieved from


  1. Gwizdka, J. Cole, M. (2012). Towards Neuro–Information Science. Proceedings of Gmunden Retreat on NeuroIS 2012. June 3-6, 2012. Gmunden, Austria. [abstract-PDF] [presentation]
  2. Balatsoukas, P., & Ruthven, I. (2012). An eye-tracking approach to the analysis of relevance judgments on the Web: The case of Google search engine. Journal of the American Society for Information Science and Technology, 63(9), 1728–1746.
  1. Fahey, D., Gedeon, T., & Zhu, D. (2011). Document Classification on Relevance: A Study on Eye Gaze Patterns for Reading. In B.-L. Lu, L. Zhang, & J. Kwok (Eds.), Neural Information Processing (pp. 143–150). Springer Berlin Heidelberg. Retrieved from
  2. Vo, T., & Gedeon, T. (2011). Reading Your Mind: EEG during Reading Task. In B.-L. Lu, L. Zhang, & J. Kwok (Eds.), Neural Information Processing (pp. 396–403). Springer Berlin Heidelberg. Retrieved from
  3. Loboda, T. D., Brusilovsky, P., & Brunstein, J. (2011). Inferring word relevance from eye-movements of readers. In Proceedings of the 16th international conference on Intelligent user interfaces (pp. 175–184). New York, NY, USA: ACM.
  1. Arapakis, I., Athanasakos, K., & Jose, J. M. (2010). A comparison of general vs personalised affective models for the prediction of topical relevance. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval (pp. 371–378). New York, NY, USA: ACM.
  1. Ajanki, A., Hardoon, D., Kaski, S., Puolamäki, K., & Shawe-Taylor, J. (2009). Can eyes reveal interest? Implicit queries from gaze patterns. User Modeling and User-Adapted Interaction, 19(4), 307–339.
  2. Arapakis, I., Moshfeghi, Y., Joho, H., Ren, R., Hannah, D., & Jose, J. M. (2009). Enriching User Profiling with Affective Features for the Improvement of a Multimodal Recommender System. In Proceedings of the ACM International Conference on Image and Video Retrieval (pp. 29:1–29:8). New York, NY, USA: ACM.  BEST STUDENT PAPER
  3. Oliveira, F. T. P., Aula, A., & Russell, D. M. (2009). Discriminating the relevance of web search results with measures of pupil size. In Proceedings of the 27th international conference on Human factors in computing systems (pp. 2209–2212). Boston, MA, USA: ACM.
  1. Klami, A., Saunders, C., de Campos, T. E., & Kaski, S. (2008). Can relevance of images be inferred from eye movements? In Proceedings of the 1st ACM international conference on Multimedia information retrieval (pp. 134–140). New York, NY, USA: ACM.
  2. Salojärvi, J. (2008). Inferring relevance from eye movements with wrong models. Teknillinen korkeakoulu. Retrieved from (ISBN 978-951-22-9613-2)

Gwizdka, J. (2012). Peeking inside a Searcher’s Brain: Prospects for Neuro-­‐Information Science. Short talk presented at the 12th Annual SIG-USE Research Symposium held at ASIS&T 2012. October, 27 2012. Baltimore, MD. [presentation handout]