Identifying clusters of user behavior in intranet search engine log files

When studying how ordinary Web users interact with Web search engines, researchers tend to either treat the users as a homogeneous group or group them according to search experience. Neither approach is sufficient, we argue, to capture the variety in behavior that is known to exist among searchers. By applying automatic clustering technique based on self‐organizing maps to search engine log files from a corporate intranet, we show that users can be usefully separated into distinguishable segments based on their actual search behavior. Based on these segments, future tools for information seeking and retrieval can be targeted to specific segments rather than just made to fit the “the average user.” The exact number of clusters, and to some extent their characteristics, can be expected to vary between intranets, but our results indicate that some more generic groups may exist. In our study, a large group of users appeared to be “fact seekers” who would benefit from higher precision, a smaller group of users were more holistically oriented and would likely benefit from higher recall, and a third category of users seemed to constitute the knowledgeable users. These three groups may raise different design implications for search‐tool developers.