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基于加权贝叶斯的数字图书资源个性化推荐方法研究

Research on Personalized Recommendation Method of Digital Book Resources Based on Weighted Bayes

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【作者】 左毅陈强杜维先

【Author】 ZUO Yi;CHEN Qiang;DU Weixian;Library, Chongqing University of Science & Technology;Research Office, Chongqing University of Science & Technology;

【机构】 重庆科技学院图书馆重庆科技学院科研处

【摘要】 传统图书资源推荐方法的推荐准确率较低,难以实现精细化和个性化推荐。因此,提出基于加权贝叶斯的数字图书资源个性化推荐方法。首先,采集数字图书信息后,利用贝叶斯分类器对个性化图书实施分类处理。其次,设置情景化偏好模块,并计算情景下读者的相似度。最后,根据相似度计算结果,利用加权贝叶斯算法为读者推荐书籍。实验结果表明,与传统方法相比,该方法的推荐准确度高且稳定性强。

【Abstract】 The accuracy of traditional book resource recommendation methods is low, making it difficult to achieve refined and personalized recommendations. Therefore, a weighted Bayesian based personalized recommendation method for digital book resources is proposed. Firstly, after collecting digital book information, a Bayesian classifier is used to classify personalized books. Secondly, set up a situational preference module and calculate the similarity of readers in the context.Finally, based on the similarity calculation results, a weighted Bayesian algorithm is used to recommend books to readers. The experimental results show that compared with traditional methods, this method has high recommendation accuracy and strong stability.

  • 【文献出处】 信息与电脑(理论版) ,Information & Computer , 编辑部邮箱 ,2023年07期
  • 【分类号】G250.7;TP391.3
  • 【下载频次】10
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