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A Personalized Information Filtering Method Based on Simple Bayesian Classifier

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 149)

Abstract

Personalized recommender systems can provide important services in a digital environment, as verified by its commercial success in book, movie, and music industries. Collaborative filtering is one of the most successful technologies in recommender systems, which predicts unknown ratings by analyzing the known ratings, and widely used in many personalized recommender areas, such as e-commerce, digital library and so on. Traditional collaborative filtering recommendation algorithm is one of the methods to solve the information overloading problem. However, with the site structure, content of the complexity and increasing number of users, there are there urgent problems in this algorithm namely data sparse, cold start and scalability. To address the sparsity problem, a personalized information filtering recommendation based on simple Bayesian classifier method is described. The algorithm used simple Bayesian classifier to smooth the rating of unrated items. It can alleviate the sparsity and improve the accurate degree of searching nearest neighbors.

Keywords

personalized information filtering simple Bayesian classifier sparsity 

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Zhejiang Business Technology InstituteNingboChina

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