• Neuropsychological Aspect of Information Retrieval (NeuroIS)

    The basis of this work was to shed a light on the nature of key concepts in information retrieval, i.e. relevance and information need from a Neuropsychological point of view. From a practical point of view this work can open a new research direction in detecting and prediction both information need and the relevance judgement of searchers by monitoring their brain activities which could lead to the next generation of information systems. The key contribution of this work was the introduction of the first neuropsychological model for the concept of relevance and information need. These three publications have won two best paper awards in ECIR 2013 and ACM SIGIR 2016. Within this work, I learnt how to design and conduct fMRI and EEG based experiments as well as how to process and analyse such big data.


    Sample Projects:

    1. Yashar Moshfeghi, Frank E. Pollick. Search Process As Transitions Between Neural States. 40th Annual ACM World Wide Web Conference (WWW’18), Lyon, France, pages 1683-1692, April 2018.
    2. Yashar Moshfeghi, Peter Triantafillou , Frank E Pollick. Understanding Information Need: an fMRI Study. 39th Annual ACM SIGIR Conference (SIGIR’16), Pisa, Italy, Pages 335-344, July 2016. (Best Paper Award).
    3. Marco Allegretti, Yashar Moshfeghi, Maria Hadjigeorgieva, Frank E Pollick, Joemon M Jose, Gabriella Pasi. When Relevance Judgement is Happening?: An EEG-based Study. 38th Annual ACM SIGIR Conference (SIGIR’15), Santiago, Chile, pages 719-722, July 2015.
  • A Game Theory Approach for Effective Crowdsource Based Relevance Assessment

    Yashar Moshfeghi, Alvaro F. H. Rosero, and Joemon M. Jose. A Game Theory
    Approach for Effective Crowdsource Based Relevance Assessment. ACM Transactions on Intelligent Systems and Technology, 7(4), 55, 2016. (doi:10.1145/2873063)