We study a new research problem, where an implicit information retrieval query is inferred from eye movements measured when the user is reading, and used to retrieve new documents. In the training phase, the user's interest is known, and we learn a mapping from how the user looks at a term to the role of the term in the implicit query. Assuming the mapping is universal, that is, the same for all queries in a given domain, we can use it to construct queries even for new topics for which no learning data is available. We constructed a controlled experimental setting to show that when the system has no prior information as to what the user is searching, the eye movements help significantly in the search. This is the case in a proactive search, for instance, where the system monitors the reading behaviour of the user in a new topic. In contrast, during a search or reading session where the set of inspected documents is biased towards being relevant, a stronger strategy is to search for content-wise similar documents than to use the eye movements.

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We have developed a prototype platform for contextual information access in mobile settings. Objects, people, and the environment are considered as contextual channels or cues to more information. The system infers, based on gaze, speech and other implicit feedback signals, which of the contextual cues are relevant, retrieves more information relevant to the cues, and presents the information with Augmented Reality (AR) techniques on a handheld or headmounted display. The augmented information becomes potential contextual cues as well, and its relevance is assessed to provide more information. In essence, the platform turns the real world into an information browser which focuses proactively on the information inferred to be the most relevant for the user. We present the first pilot application, a Virtual Laboratory Guide, and its early evaluation results.

In the absence of explicit queries, an alternative is to try to infer users' interests from implicit feedback signals, such as clickstreams or eye tracking. The interests, formulated as an implicit query, can then be used in further searches. We formulate this task as a probabilistic model, which can be interpreted as a kind of transfer or meta-learning. The probabilistic model is demonstrated to outperform an earlier kernel-based method in a small-scale information retrieval task.

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