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Evaluating distance-based clustering for user (browse and click) sessions in a domain-specific collection

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Abstract

We seek to improve information retrieval in a domain-specific collection by clustering user sessions from a click log and then classifying later user sessions in real time. As a preliminary step, we explore the main assumption of this approach: whether user sessions in such a site are related to the question that they are answering. Since a large class of machine learning algorithms use a distance measure at the core, we evaluate the suitability of common machine learning distance measures to distinguish sessions of users searching for the answer to same or different questions. We found that two distance measures work very well for our task and three others do not. As a further step, we then investigate how effective the distance measures are when used in clustering. For our dataset, we conducted a user study where we had multiple users answer the same set of questions. This data, grouped by question, was used as our gold standard for evaluating the clusters produced by the clustering algorithms. We found that the observed difference between the two classes of distance measures affected the quality of the clusterings, as expected. We also found that one of the two distance measures that worked well to differentiate sessions, worked significantly better than the other when clustering. Finally, we discuss why some distance metrics performed better than others in the two parts of our work.

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Acknowledgments

We acknowledge the support of the Danish Cancer Society and Mr. Tor Øyan, our contact. We also received support from the National Science Foundation, award 0812260. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the NSF. We thank Ms. Tesca Fitzgerald, Ms. Suzanna Kanga, Ms. Flery Decker, and Jonathon Britell, MD, Board Certified Oncologist.

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Authors and Affiliations

  1. Department of Computer Science, Portland State University, Portland, OR, USA

    Jeremy Steinhauer & Lois M. L. Delcambre

  2. Department of Communication and Psychology, Aalborg University, Aalborg, Denmark

    Marianne Lykke

  3. Department of Library and Information Science, Oslo University College, Oslo, Norway

    Marit Kristine Ådland

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  1. Jeremy Steinhauer
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  2. Lois M. L. Delcambre
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  3. Marianne Lykke
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  4. Marit Kristine Ådland
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Correspondence to Jeremy Steinhauer.

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Steinhauer, J., Delcambre, L.M.L., Lykke, M. et al. Evaluating distance-based clustering for user (browse and click) sessions in a domain-specific collection. Int J Digit Libr 14, 167–179 (2014). https://doi.org/10.1007/s00799-014-0117-z

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