计算机科学 ›› 2018, Vol. 45 ›› Issue (5): 190-195.doi: 10.11896/j.issn.1002-137X.2018.05.032
王劲松,蔡朝晖,李永凯,刘树波
WANG Jing-song, CAI Zhao-hui, LI Yong-kai and LIU Shu-bo
摘要： 传统的协同过滤相似性度量方法主要考虑用户评分之间的相似性,缺少对评分差异性的考虑。文中 将用户评分关系分为差异部分和相关部分,提出了一种基于用户评分差异性和相关性的相似性度量方法。该方法在非极其稀疏数据集下有较好的推荐效果。针对该方法在稀疏数据集下存在推荐不准确的问题,采用预填充方法对其进行改进。实验表明,该方法在预填充后的推荐精度得到明显提高。
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