计算机科学 ›› 2018, Vol. 45 ›› Issue (1): 90-96.doi: 10.11896/j.issn.1002-137X.2018.01.014
• CRSSC-CWI-CGrC-3WD 2017 • 上一篇 下一篇
叶晓庆,刘盾,梁德翠
YE Xiao-qing, LIU Dun and LIANG De-cui
摘要： 为了降低传统协同过滤算法的推荐成本,并解决该算法评分信息单一的问题,提出了一种基于协同过滤的三支粒推荐算法。该算法在传统协同过滤的基础上,考虑项目特征对用户评分的影响,根据项目特征、粒化用户项目评分矩阵,形成用户对项目粒度的评分矩阵,并以此作为用户偏好的测度依据。同时,该算法在推荐过程中引入三支决策,考虑了推荐过程中产生的误分类成本和学习成本,并基于用户真实的评分偏好构建三支推荐。实验结果显示,基于协同过滤的三支粒推荐算法与传统协同过滤算法相比,不但提高了算法的推荐质量,而且降低了推荐成本。
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