Determining web pages similarity using distributed learning automata and graph partitioning

Determining similarity between web pages is a key factor for the success of many web mining applications such as recommendation systems and adaptive web sites. In this paper, we propose a new hybrid method of distributed learning automata and graph partitioning to determine similarity between web pages using the web usage data. The idea of the proposed method is that if different users request a couple of pages together, then these pages are likely to correspond to the same information needs therefore can be considered similar. In the proposed method, a learning automaton is assigned to each web page and tries to find the similarities between that page and other pages of a web site utilizing the results of a graph partitioning algorithm performed on the graph of the web site. Computer experiments show that the proposed method outperforms Hebbian algorithm and the only learning automata based method reported in the literature.