Mining Web data to create online navigation recommendations

A system to provide online navigation recommendation for Web visitors is introduced. We call visitor the anonymous user, i.e., when only data about her/his browsing behavior (Web logs) are available. We first apply clustering techniques over a large sample of Web data. Next, from the significant patterns that are discovered, a set of rules about how to use them is created. Finally, comparing the current Web visitor session with the patterns, online navigation recommendations are proposed using the mentioned rules. The system was tested using data from a real Web site, showing its effectiveness.

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