摘要
【目的】针对Slope One算法未考虑项目相似性、项目属性和对目标用户已有评分同等考虑进而导致推荐准确度降低的问题进行改进。【方法】提出一种基于改进的项目相似性度量、改进的项目属性相似性度量和用户评分概率函数的多权值的Slope One协同过滤算法,在项目相似性度量方面将共同评价的两个项目的用户数量和Pearson相关系数相融合,在项目属性相似性度量方面将修正的拉普拉斯平滑与Jaccard系数相结合,同时利用用户评分概率函数对用户已有评分进行有效区分。【结果】实验结果表明,本文方法相比于原Slope One算法,MAE值下降了5.4%,能够获得更好的推荐准确度。【局限】只关注推荐系统中用户对项目产生的评分,并没有关注用户对项目给出的评论,在一定程度上影响了推荐效果。【结论】本文方法更能适应评分数据稀疏性,有效提高了推荐系统的推荐质量。
[Objective] This paper aims to increase the recommendation accuracy with the help of modified Slope One algorithm. [Methods] We proposed a Slope One Collaboration Filtering Algorithm based on multi-weights, which improved the items＇ similarity measure, attributes similarity measure and users＇ rating probability function. Then, we combined the items＇ similarity measure with the number of users and Pearson correlation coefficient, the items＇ attributes similarity measure with modified Laplacian smoothing and Jaccard coefficient. We also identified users＇ ratings with a new probability function. [Results] The proposed method reduced the MAE by 5.4%, which increased the recommendation accuracy. [Limitations] The new method did not examine the users＇ comments, which might pose some negative effects to the recommendation accuracy. [Conclusions] The proposed algorithm could effectively improve the service of recommendation systems.
出处
《数据分析与知识发现》
CSSCI
CSCD
2017年第6期65-71,共7页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金青年基金项目"基于马尔科夫树与DRT的汉语句群自动划分算法研究"(项目编号:61202281)
教育部人文社科规划青年基金"基于语义相关性的汉语组块切分模型研究"(项目编号:12YJCZH201)的研究成果之一
