Abstract
Recommendation systems are services which recommend users new items such as news articles, books, music, and movie they would like. With the rapid development of information technology especially worldwide web, the information on the internet is exploring, how to screen out the information needed from multitude data in internet becomes a primary problem for solution. To solve this problem, a Bayesian network and Trust model based movie recommendation system is proposed, the Bayesian network is imported for user preference modeling and trust model is used to filter the recommending history data and enable the system to tolerant the noisy data. The simulation experiment use the movielens dataset as a source and validate the validity of the algorithm described in this paper, and a conclusion is reached that compared with collaborative filtering algorithm, the algorithm proposed holds advantage in the field of efficiency and noisy tolerant capability.
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Wei, D., Junliang, C. (2013). The Bayesian Network and Trust Model Based Movie Recommendation System. In: Du, Z. (eds) Intelligence Computation and Evolutionary Computation. Advances in Intelligent Systems and Computing, vol 180. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31656-2_107
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DOI: https://doi.org/10.1007/978-3-642-31656-2_107
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31655-5
Online ISBN: 978-3-642-31656-2
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