The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first-of-its-kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology-based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).
|Number of pages||14|
|Journal||JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY|
|Publication status||Published - 12 Mar 2019|
|MoE publication type||A1 Journal article-refereed|
- information retrieval
- brain-computer interfaces
- interactive intent modeling
- relevance feedback
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Probabilistic user modelling methods for improving human-in-the-loop machine learning for prediction
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