安徽大学学报（自然科学版）Issue(6)：23-29,7.DOI:10.3969/j.issn.1000-2162.2014.06.004
基于项目属性和局部优化的协同过滤推荐算法
Collaborative filtering recommendation algorithm based on item attribute and local optimization
摘要
Abstract
To overcome the impact of data sparsity on traditional collaborative filtering,we presented collaborative filtering recommendation algorithm based on item attribute and local optimization,named CUCF.We firstly used the improved j accard coefficient to optimize the similarity of item scoring.Then,we employed the Laplace smoothing method to get the similarity of item attribute.Finally,we made a linear combination of these two similarity results of items,and then used local optimization options to select neighbors as a reference for the target group. Our experimental results showed that the CUCF algorithm could reduce the negative impact of data sparsity on recommendations and effectively lower the mean absolute error of prediction consequences.Our experiments further contrast CUCF with the other four different recommendation methods,the precision of prediction was increased from 7.1% to 1 5.5%.It proved that in terms of prediction accuracy, the CUCF algorithm could achieve better results.关键词
拉普拉斯平滑/项目属性/局部优化/协同过滤Key words
Laplace smoothing/item attribute/local optimization/collaborative filtering分类
信息技术与安全科学引用本文复制引用
刘慧婷,陈超,吴共庆,赵鹏..基于项目属性和局部优化的协同过滤推荐算法[J].安徽大学学报（自然科学版）,2014,(6):23-29,7.基金项目
国家863计划课题“多源异构数据集成与挖掘的关键技术研究”资助项目(2012AA011005) （2012AA011005）
国家自然科学基金资助项目(61202227) （61202227）
安徽省自然科学基金资助项目(1408085MF122) （1408085MF122）
