How Much is Too Much?: Whole Session vs. First Query Behaviors in Task Type Prediction

Matthew Mitsui, Jiqun Liu, Chirag Shah

Abstract: One of the emerging and important problems in Interactive Information Retrieval research is predicting search tasks. Given a searcher's behavior during a search session, can the searcher's task be predicted? Which aspects of the task can be predicted, and how quickly and how accurately can the prediction be made? Much past literature has examined relationships between browsing behavior and task type at a statistical level, and recent work is moving towards prediction. While one may think whole session measures are useful for prediction, recent findings on common measures have suggested the contrary. Can less of the session still be useful? We examine the opposite end: the first query. Using multiple data sets for comparison, our results suggest that first query measures can be at least as good as -- and sometimes better than -- whole session measures for certain task type predictions.