计算机科学 ›› 2018, Vol. 45 ›› Issue (5): 190-195.doi: 10.11896/j.issn.1002-137X.2018.05.032

• 人工智能 • 上一篇    下一篇

基于用户评分差异性和相关性的协同过滤推荐算法

王劲松,蔡朝晖,李永凯,刘树波   

  1. 武汉大学计算机学院 武汉430072,武汉大学计算机学院 武汉430072,武汉大学计算机学院 武汉430072,武汉大学计算机学院 武汉430072
  • 出版日期:2018-05-15 发布日期:2018-07-25
  • 基金资助:
    本文受国家自然科学基金(41671443)资助

Collaborative Filtering Recommendation Algorithm Based on Difference and Correlation of Users’ Ratings

WANG Jing-song, CAI Zhao-hui, LI Yong-kai and LIU Shu-bo   

  • Online:2018-05-15 Published:2018-07-25

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。

关键词: 协同过滤推荐,差异性,相关性,预填充

Abstract: The traditional similarity measurement in collaborative filtering mainly pays attention to the similarity between users’ ratings,lacking the consideration of difference of users’ ratings.This paper divided the relationship of users’ ratings into differential part and correlated part,and proposed a similarity measurement based on the difference and the correlation of users’ ratings on the non-sparse dataset.In order to solve the problem that the algorithm’s recommendation is not accurate in spare dataset,this paper improved this algorithm by prefilling the vacancy of rating matrix.Experiment results show that this algorithm can significantly improve the accuracy of recommendation after prefil-ling the rating matrix.

Key words: Collaborative filtering recommendation,Difference,Correlation,Prefilling

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