Predicting web user behavior using learning-based ant colony optimization

An ant colony optimization-based algorithm to predict web usage patterns is presented. Our methodology incorporates multiple data sources, such as web content and structure, as well as web usage. The model is based on a continuous learning strategy based on previous usage in which artificial ants try to fit their sessions with real usage through the modification of a text preference vector. Subsequently, trained ants are released onto a new web graph and the new artificial sessions are compared with real sessions, previously captured via web log processing. The main results of this work are related to an effective prediction of the aggregated patterns of real usage, reaching approximately 80%. In the second place, this approach allows the obtaining of a quantitative representation of the keywords that influence the navigational sessions.