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Web log data has been the basis for analyzing user query session behavior for a number of years, but it has several important shortcomings. The main one being that we do not really know what the user is doing -- is s/he looking at the screen or doing something else? We have conducted an Eye-Tracking study to analyze user behavior when searching the web and looking for specific information on results and content pages. The goal is to obtain more precise information about the search strategy of the user. Which characteristics make the difference between successful and unsuccessful searches? This research presents results focusing on the number of formulated queries by session, documents clicked, the fixation durations on the documents, and the distribution of the attention in the different areas of the screen, among other aspects.

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An important trend for e-commerce sites is to be able to provide a
personalized experience for the consumer, and there has been an
increasing research interest in Web usage mining techniques to that
end. One of the current problems of Web usage mining deals with
privacy and anonymity. The data it uses, contains sensitive
information that should be protected. We introduce the
microaggregation of Web usage data to ensure its anonymity and
privacy while maintaining its usefulness for usage mining.

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