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Predicting information searchers' topic knowledge at different search stages
Author(s) -
Liu Jingjing,
Liu Chang,
Belkin Nicholas J.
Publication year - 2016
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23606
Subject(s) - computer science , information retrieval , personalization , session (web analytics) , point (geometry) , information seeking , world wide web , geometry , mathematics
As a significant contextual factor in information search, topic knowledge has been gaining increased research attention. We report on a study of the relationship between information searchers' topic knowledge and their search behaviors, and on an attempt to predict searchers' topic knowledge from their behaviors during the search. Data were collected in a controlled laboratory experiment with 32 undergraduate journalism student participants, each searching on 4 tasks of different types. In general, behavioral variables were not found to have significant differences between users with high and low levels of topic knowledge, except the mean first dwell time on search result pages. Several models were built to predict topic knowledge using behavioral variables calculated at 3 different stages of search episodes: the first‐query‐round, the middle point of the search, and the end point. It was found that a model using some search behaviors observed in the first query round led to satisfactory prediction results. The results suggest that early‐session search behaviors can be used to predict users' topic knowledge levels, allowing personalization of search for users with different levels of topic knowledge, especially in order to assist users with low topic knowledge.