Identifying Web search session patterns using cluster analysis: A comparison of three search environments

Abstract

AbstractSession characteristics taken from large transaction logs of three Web search environments (academic Web site, public search engine, consumer health information portal) were modeled using cluster analysis to determine if coherent session groups emerged for each environment and whether the types of session groups are similar across the three environments. The analysis revealed three distinct clusters of session behaviors common to each environment: “hit and run” sessions on focused topics, relatively brief sessions on popular topics, and sustained sessions using obscure terms with greater query modification. The findings also revealed shifts in session characteristics over time for one of the datasets, away from “hit and run” sessions toward more popular search topics. A better understanding of session characteristics can help system designers to develop more responsive systems to support search features that cater to identifiable groups of searchers based on their search behaviors. For example, the system may identify struggling searchers based on session behaviors that match those identified in the current study to provide context sensitive help.