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Learning Web Page Scores by Error Back-Propagation

Learning Web Page Scores by Error Back-Propagation,Michelangelo Diligenti,Marco Gori,Marco Maggini

Learning Web Page Scores by Error Back-Propagation   (Citations: 17)
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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 la- bels. 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 back- propagation through the graph is devised to learn the model parameters. The trained model can as- sign scores to the graph nodes generalizing the cri- teria 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 tech- nique in reorganizing the rank accordingly to the examples provided in the training set.
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