A Robust Biclustering Approach for Effective Web Personalization

Abstract

Web recommendation or personalization could be viewed as a process that recommends the customized web presentations or predicts the tailored web contents to web users according to their specific need. The first step in intelligent web personalization is segmenting web log data into web user sessions for constructing user model. These segments are later used to recommend relevant URLs to old and new anonymous users of a web site. The knowledge discovery part can be executed offline by periodically mining new contents of the user access log files. The recommendation part is the online component of a usage-based personalization system. In this study, we propose a robust Biclustering algorithm to disclose the correlation that exists between users and pages. This chapter proposes a Robust Biclustering (RB) method based on constant values for integrating user clustering and page clustering techniques which is followed by a recommendation system that can respond to the users’ individual interests. To evaluate the effectiveness and efficiency of the recommendation, experiments are conducted in terms of the recommendation accuracy metric. The experimental results have demonstrated that the proposed Biclustering method is very simple and is able to efficiently extract needed usage knowledge accurately for web page recommendation.