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A Real-Time Eye Tracking Based Query Expansion Approach via Latent Topic Modeling

Chen, Yongqiang; Zhang, Peng; Song, Dawei and Wang, Benyou (2015). A Real-Time Eye Tracking Based Query Expansion Approach via Latent Topic Modeling. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, ACM, New York, pp. 1719–1722.

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Formulating and reformulating reliable textual queries have been recognized as a challenging task in Information Retrieval (IR), even for experienced users. Most existing query expansion methods, especially those based on implicit relevance feedback, utilize the user's historical interaction data, such as clicks, scrolling and viewing time on documents, to derive a refined query model. It is further expected that the user's search experience would be largely improved if we could dig out user's latent query intention, in real-time, by capturing the user's current interaction at the term level directly. In this paper, we propose a real-time eye tracking based query expansion method, which is able to: (1) automatically capture the terms that the user is viewing by utilizing eye tracking techniques; (2) derive the user's latent intent based on the eye tracking terms and by using the Latent Dirichlet Allocation (LDA) approach. A systematic user study has been carried out and the experimental results demonstrate the effectiveness of our proposed methods.

Item Type: Conference or Workshop Item
Copyright Holders: 2015 ACM
ISBN: 1-4503-3794-5, 978-1-4503-3794-6
Project Funding Details:
Funded Project NameProject IDFunding Body
Not Set2013CB329304Chinese 973 Program
Not Set2014CB744604Chinese 973 Program
Not Set2015AA015403Chinese 863 Program
Not Set61272265Natural Science Foundation of China
Not Set61402324Natural Science Foundation of China
Not Set20130032120044Research Fund for the Doctoral Program of Higher Education of China
Keywords: eye tracking; query expansion; real time; implicit relevance feedback; LDA
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Related URLs:
Item ID: 44135
Depositing User: Dawei Song
Date Deposited: 24 Aug 2015 10:20
Last Modified: 07 Dec 2018 20:19
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