Abstract
In this article, a method that models user navigation on the web, as opposed to a single website, is presented, aiming to assist the user by recommending pages. User modeling is done through data mining of web usage logs, resulting in aggregate, rather than personal models. The proposed approach extends grammatical inference methods by introducing an extra merging criterion, which examines the semantic similarity of automaton states. The experimental results showed that the method does indeed facilitate the modeling of web navigation, which was not possible with the existing web usage mining methods. However, a content-based recommendation model is shown to still outperform the proposed method, which suggests that the knowledge of the navigation sequence does not contribute to the recommendation process. This is due to the thematic cohesion of navigation sessions, in comparison to the large thematic diversity of web usage data. Among three variants of the proposed method, the one based on Blue Fringe, that examines a larger space of possible merges, performs better.
Published Version
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