Automatic Web User Profiling and Personalization Using Robust Fuzzy Relational Clustering

Abstract

The proliferation of information on the world wide Web has made the personalization of this information space a necessity. Personalization of content returned from a Web site is a desired feature that can enhance server performance improve system design, and lead to wise marketing decisions in electronic commerce. Mining typical user profiles from the vast amount of historical data stored in access logs is an important component of Web personalization. In the absence of a priori knowledge, unsupervised or clustering methods seem to be ideally suited to categorize the usage behavior of Web surfers. In this chapter, we present a framework for mining typical user profiles from server acces logs based on robust fuzzy relational clustering. As a by-product of the clustering process that generates robust profiles, associations between different URL addresses on a given site can easily be inferred. In general, the URLs that are present in the same profile tend to be visited together in the same session or form a large itemset. Finally, we present a personalization system that uses previously mined profiles to automatically generate a Web page containing URLs the user might be interested in. Our personalization approach is based on profiles computed from the prior traversal patterns of the users on the website and do not involve providing any declarative private information or the user to log in.KeywordsFuzzy ClusterUser SessionRelational ClusterIntercluster DistanceRobust WeightThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.