Using Ontology and Sequence Information for Extracting Behavior Patterns from Web Navigation Logs

Many e-commerce web sites such as online book retailers or specialized information hubs such as online movie databases make use of recommendation systems where users are directed to items of interests based on past user interactions. While keyword based approaches are naive and do not take content or context into account, collaborative filtering and content-based techniques suffer from biased ratings, first item and first-rater problems. Recent approaches try to incorporate underlying semantic properties of data by employing ontology based usage mining. This work aims to design a Web navigation behavior extraction approach that makes use of page semantics and navigation sequence information where web pages are seen as objects and sessions as sequence of objects. Instead of relying on users' content ratings, user sessions are clustered on a semantic level to capture different behavioral groups. Since semantic information and sequence are used for the clustering distance function, each cluster represents a behavior group instead of simple data groups. In this work, we use the recommendation results as a means for measuring the effectiveness of the clusters we have generated. The effect of integrating semantic information and page access sequence into the patterns are evaluated with a set of experiments.