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An Online Personalized Recommendation Model Based on Bayesian Networks

IFIP Advances in Information and Communication Technology (AICT)
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  • Mathematics


It is one of an important method of using Bayesian networks in electronic commercial recommended system. But the models of Bayesian networks for describing recommended system have a problem that it could not learn online. The paper puts forward an online personalized recommended model based on Bayesian networks. The paper uses a partial ordering to represent previous structure and find posterior distributions of every node on the orders to realize online structure learning. It also uses a correctional function to revise log likelihood for online parameter learning. The experiment shows that the model can be learned online for personalized recommended system. Full Text at Springer, may require registration or fee

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