Evaluation of Web Usage Mining Approaches for User's Next Request Prediction

摘要

Analysis of Web server logs is one of the important challenge to provide Web intelligent services. In this paper, we describe a framework for a recommender system that predicts the user's next requests based on their behaviour discovered from Web Logs data. We compare results from three usage mining approaches: association rules, sequential rules and generalised sequential rules. We use two selection rules criteria: highest confidence and last-subsequence. Experiments are performed on three collections of real usage data: one from an Intranet Web site and two from an Internet Web site.