Information Retrieval by Inferring Implicit Queries from Eye Movements

David R. Hardoon, John Shawe-Taylor, Antti Ajanki, Kai Puolamäki, Samuel Kaski

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    11 Citations (Scopus)


    We introduce a new search strategy, in which the information retrieval (IR) query is inferred from eye movements measured when the user is reading text during an IR task. In training phase, we know the users’ interest, that is, the relevance of training documents. We learn a predictor that produces a “query” given the eye movements; the target of learning is an “optimal” query that is computed based on the known relevance of the training documents. Assuming the predictor is universal with respect to the users’ interests, it can also be applied to infer the implicit query when we have no prior knowledge of the users’ interests. The result of an empirical study is that it is possible to learn the implicit query from a small set of read documents, such that relevance predictions for a large set of unseen documents are ranked significantly better than by random guessing.
    Original languageEnglish
    Title of host publicationEleventh International Conference on Artificial Intelligence and Statistics, San Juan, Puerto Rico, 2007
    EditorsMarina Meila, Xiaotong Shen
    Publication statusPublished - 2007
    MoE publication typeA4 Article in a conference publication
    EventInternational Conference on Artificial Intelligence and Statistics - San Juan, Puerto Rico
    Duration: 21 Mar 200724 Mar 2007
    Conference number: 11


    ConferenceInternational Conference on Artificial Intelligence and Statistics
    Country/TerritoryPuerto Rico
    CitySan Juan


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