Users giving relevance feedback in exploratory search are often uncertain
about the correctness of their feedback, which may result in noisy or even
erroneous feedback. Additionally, the search intent of the user may be volatile
as the user is constantly learning and reformulating her search hypotheses
during the search. This may lead to a noticeable concept drift in the feedback.
We formulate a Bayesian regression model for predicting the accuracy of each
individual user feedback and thus find outliers in the feedback data set.
Additionally, we introduce a timeline interface that visualizes the feedback
history to the user and gives her suggestions on which past feedback is likely
in need of adjustment. This interface also allows the user to adjust the
feedback accuracy inferences made by the model. Simulation experiments
demonstrate that the performance of the new user model outperforms a simpler
baseline and that the performance approaches that of an oracle, given a small
amount of additional user interaction. A user study shows that the proposed
modelling technique, combined with the timeline interface, makes it easier for
the users to notice and correct mistakes in their feedback, and to discover new
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