Abstract
Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user’s interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recommending information by relevance inferred directly from brain signals. In experiments, participants were asked to read Wikipedia documents about a selection of topics while their EEG was recorded. Based on the prediction of word relevance, the individual’s search intent was modeled and successfully used for retrieving new relevant documents from the whole English Wikipedia corpus. The results show that the users’ interests toward digital content can be modeled from the brain signals evoked by reading. The introduced brain-relevance paradigm enables the recommendation of information without any explicit user interaction and may be applied across diverse information-intensive applications.
Highlights
To infer the informational intentions of the individual and to retrieve new information that matches these intentions
Grand-average-based Event-related potentials (ERPs) results show that brain activity associated with relevant words is different from brain activity associated with irrelevant words (H1) over all participants and reading tasks
By combining insights on information science and cognitive neuroscience, we proposed the brain-relevance paradigm to construct maximally natural interfaces for information filtering: The user reads, brain activity is monitored, and new information is recommended
Summary
To infer the informational intentions (i.e., the intent model5) of the individual and to retrieve new information that matches these intentions. A system utilizing the brain-relevance paradigm and using the presented end-to-end methodology can mitigate the requirement of explicit human-computer interaction to convey relevance feedback on individual words directly from brain activity during a reading task. The reactive states are naturally evoked by reading relevant words and the user is not required to perform any additional, explicit tasks (e.g., mental counting of relevant words or imaginary motor activity) that have been previously shown to enhance the signal-to-noise ratio[7]. The result suggests that relevance can be predicted from brain signals that are naturally evoked when users read, and they can be utilized in recommending new information from the Web as a part of our everyday information-seeking activities. The following two sections provide motivation based on both cognitive neuroscience and information science, followed by existing foundations of the brain-relevance paradigm
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