Web Search Clickstreams

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