A new recommendation algorithm using distributed learning automata and graph partitioning

Recommendation systems aim at directing users toward the resources that best meet their needs and interests. In this paper, we propose a new recommendation algorithm based on a hybrid method of distributed learning automata and graph partitioning. The proposed method utilizes usage data and hyperlink graph of the web site. The idea of the proposed method is that an appropriate recommendation for a user can be pages similar to the pages the user has already visited. To calculate similarities between pages, it is assumed that if different users request a couple of pages together, these pages are likely to correspond to the same information need therefore can be considered similar. Experiments on synthetic and real data show that the proposed algorithm provides better recommendations than the only learning automata based recommendation method reported in the literature.