信息技术的飞速发展使得旅游信息呈爆炸式增长,如何根据游客的特征和偏好,进行旅游信息的智能化推荐已十分必要。MI-NB算法是在传统贝叶斯分类算法不足的基础上进行改进的,应用了互信息的知识,通过相对可信度R来进行特征选择,以删除冗余属性,并把R作为权值引入到NB算法中,从而得到改进后的MI-NB算法。采用改进的贝叶斯分类算法（MI-NB）来进行旅游景点的推荐,能够大大降低分类数据的维数,有效提高了景点推荐的准确率。%With the rapid development of information technology,tourism information has explosively increased.Thus,it is very imperative to have the research and design of an intelligence recommendation system of traveling information according to the visitors＇ characteristics and preferences.MI-NB algorithm,which is based on the shortage of traditional Bayesian classification algorithm and the mutual information knowledge,is proposed in this paper.The relative credibility R is considered as the standard of feature selection to remove redundant attributes and is introduced into NB algorithm as the weight to obtain the improved MI-NB algorithm.Experiments showed that MI-NB algorithms could greatly reduce the dimension of data and effectively improve the accuracy of recommendation of tourist attractions.
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