Predicting users' domain knowledge from search behaviors

Authors:
Xiangmin Zhang;Michael Cole;Nicholas Belkin
Affiliations:
Wayne State University, Detroit, MI, USA;Rutgers University, New Brunswick, USA;Rutgers University, New Brunswick, USA
Venue:
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Year:
2011

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Abstract

This study uses regression modeling to predict a user's domain knowledge level (DK) from implicit evidence provided by certain search behaviors. A user study (n=35) with recall-oriented search tasks in the genomic domain was conducted. A number of regression models of a person's DK, were generated using different behavior variable selection methods. The best model highlights three behavior variables as DK predictors: the number of documents saved, the average query length, and the average ranking position of documents opened. The model is validated using the split sampling method. Limitations and future research directions are discussed.