Personalized web recommendation based on path clustering

Abstract

Each user accesses a Website with certain interests. The interest can be manifested by the sequence of each Web user access. The access paths of all Web users can be clustered. The effectiveness and efficiency are two problems in clustering algorithms. This paper provides a clustering algorithm for personalized Web recommendation. It is path clustering based on competitive agglomeration (PCCA). The path similarity and the center of a cluster are defined for the proposed algorithm. The algorithm relies on competitive agglomeration to get best cluster numbers automatically. Recommending based on the algorithm doesn't disturb users and needn't any registration information. Experiments are performed to compare the proposed algorithm with two other algorithms and the results show that the improvement of recommending performance is significant. © Springer-Verlag Berlin Heidelberg 2006.