A Robust Biclustering Approach for Effective Web Personalization

A Robust Biclustering Approach for Effective Web Personalization

H. Inbarani (Periyar University, India) and K. Thangavel (Periyar University, India)
DOI: 10.4018/978-1-60960-102-7.ch011
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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.
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2. Background

The stages of page recommender systems include preprocessing, segmenting web log data into web user sessions, and learning a usage model from this data. The usage model can come in many forms: from the modeling used in collaborative filtering, that simply stores all other users’ information and then relies on K Nearest Neighbors to provide recommendations from previous history of neighbors or similar users, to a set of frequent itemsets and associations, to a set of clusters of the user sessions, and resulting web user profiles or summaries(Olfa Nasraoui, 2003).

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