Applying Web Usage Mining Techniques to Design Effective Web Recommendation Systems: A Case Study

Abstract

Recommender systems are helpful tools which provide an adaptive Web environment for Web users. Recently, a number of Web page recommender systems have been developed to extract the user behavior from the user’s navigational path and predict the next request as he/she visits Web pages. Web Usage Mining (WUM) is a kind of data mining method that can be used to discover this behavior of user and his/her access patterns from Web log data. This paper first presents an overview of the used concepts and techniques of WUM to design Web recommender systems. Then it is shown that how WUM can be applied to Web server logs for discovering access patterns. Afterward, we analyze some of the problems and challenges in deploying recommender systems. Finally, we propose the solutions which address these problems.