重庆邮电大学学报（自然科学版）2016，Vol.28Issue(4)：518-524,7.DOI:10.3979/j.issn.1673-825X.2016.04.012
融合标签相似度的k近邻Slope One算法
K-nearest neighbor hybrid Slope One algorithm combined with tag similarity
张鹏 1葛小青2
作者信息
- 1. 中国科学院 遥感与数字地球研究所，北京 100094
- 2. 中国科学院大学，北京 100049
- 折叠
摘要
Abstract
Slope One Collaborative Filtering algorithm is widely used in personalized recommendation system.Label is an important form to describe the characteristics of the items.To overcome its deficiency in rating prediction accuracy,this pa-per proposes a new hybrid algorithm combined with tag information.With reference to the k-nearest neighbor Collaborative Filtering algorithm,we select neighbors of the target item to participate in the calculation of the average rating deviation, which ensures computational efficiency and improves the prediction accuracy.The algorithm defines rating similarities and tag similarities as weight to revise the linear regression model.To achieve further improvement of the recommendation quali-ty,the algorithm adopts a linear weighted fusion method to combine the results.Experimental results on the Movielens data sets indicated that,compared with the traditional weighted Slope One algorithm,mean average absolute error declined 4.8%,while recall rate and precision rate respectively increased 32.1% and 26.3%.关键词
协同过滤/推荐系统/标签相似度/k近邻/Slope One算法Key words
collaborative filtering/recommendation system/tag similarity/k-nearest neighbor/Slope One分类
信息技术与安全科学引用本文复制引用
张鹏,葛小青..融合标签相似度的k近邻Slope One算法[J].重庆邮电大学学报（自然科学版）,2016,28(4):518-524,7.
