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In this paper we introduce a novel approach for modeling and interpreting search behavior for exploratory search by using a so called exploration graph. We use an existing methodology of logging and analyzing user interactions with a web browser and add an additional interpretation step that can be used, e. g. to integrate sensemaking or browsing patterns into the log data. We conducted a user study and are able to show that: (a) interaction logs can be interpreted semantically, (b) semantic interpretations lead to a more connected exploration graph, and (c) multiple (even contradicting) interpretations of the same search behavior may exist at the same time. We also show how our theoretical model can be applied in the area of professional search by incorporating insights gained from the model into novel recommendation and machine learning approaches.

Interpreting and Leveraging Browser Interaction for Exploratory Search Tasks