计算机应用与软件2017，Vol.34Issue(8)：265-269,275,6.DOI:10.3969/j.issn.1000-386x.2017.08.047
一种巴氏系数改进相似度的协同过滤算法
COLLABORATIVE FILTERING ALGORITHM BASED ON IMPROVED SIMILARITY MEASURE WITH BHATTACHARYYA COEFFICIENT
摘要
Abstract
Aiming at the problem of low-quality recommendation and data sparsity, we proposed a collaborative filtering algorithm based on improved similarity measure with Bhattacharyya coefficient.First, we use Jaccard similarity to calculate the global similarity between users based on neighbor cooperative filtering algorithm.Secondly, we use the Bhattacharyya coefficient to obtain the whole law of the grade distribution.And we combine the Pearson correlation coefficient to calculate the local similarity.Finally, we fuse the global similarity and local similarity to obtain final similarity metric.The experimental results show that algorithm can get better recommendation results on sparse data sets.It effectively mitigates the sparseness of scoring data and improves the recommended accuracy.关键词
协同过滤/数据稀疏性/巴氏系数/相似度计算Key words
Collaborative filtering/Data sparsity/Bhattacharyya coefficient/Similarity measure分类
信息技术与安全科学引用本文复制引用
武文琪,王建芳,张朋飞,刘永利..一种巴氏系数改进相似度的协同过滤算法[J].计算机应用与软件,2017,34(8):265-269,275,6.基金项目
国家自然科学基金项目(61202286) （61202286）
河南省高等学校青年骨干教师资助计划项目(2015GGJS-068). （2015GGJS-068）
