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Abstract:
Search engines are a vital part of the Web and thus the Internet
infrastructure. Therefore understanding the behavior of users searching the Web
gives insights into trends, and enables enhancements of future search
capabilities. Possible data sources for studying Web search behavior are either
server-side logs or client-side logs. Unfortunately, current server-side logs
are hard to obtain as they are considered proprietary by the search engine
operators. Therefore we in this paper present a methodology for extracting
client-side logs from the traffic exchanged between a large user group and the
Internet. The added benefit of our methodology is that we do not only extract
the search terms, the query sequences, and search results of each individual
user but also the full \textit{clickstrea}, i.e., the result pages users view
and the subsequently visited hyperlinked pages. We propose a finite-state
Markov model that captures the user web searching and browsing behavior and
allows us to deduce users' prevalent search patterns. To our knowledge, this is
the first such detailed client-side analysis of clickstreams.
