Learning Web Page Scores by Error Back-Propagation

Michelangelo Diligenti, Marco Gori, Marco Maggini

Abstract: In this paper we present a novel algorithm to learn a score distribution over the nodes of a labeled graph (directed or undirected). Markov Chain theory is used to define the model of a random walker that converges to a score distribution which depends both on the graph connectivity and on the node labels. A supervised learning task is defined on the given graph by assigning a target score for some nodes and a training algorithm based on error backpropagation through the graph is devised to learn the model parameters. The trained model can assign scores to the graph nodes generalizing the criteria provided by the supervisor in the examples. The proposed algorithm has been applied to learn a ranking function for Web pages. The experimental results show the effectiveness of the proposed technique in reorganizing the rank accordingly to the examples provided in the training set.