计算机应用与软件Issue(12)：323-328,6.DOI:10.3969/j.issn.1000-386x.2014.12.079
基于组合相似度的优化协同过滤算法
AN OPTIMISED COLLABORATIVE FILTERING ALGORITHM BASED ON COMBINED SIMILARITY
摘要
Abstract
Collaborative filtering is a most widely used recommendation technique in personalised recommendation system.With the increase in numbers of user and item, the sparsity of data becomes an important factor affecting the recommendation quality.Therefore, the six-type combined similarities are presented, which combines two traditional similarity metrics of the adjusted cosine similarity and the Pearson correlation with the structure similarity metrics such as Jaccard coefficient, Salton coefficient and IUF coefficient.Experiment done on MovieLens shows that the combined similarity-based optimised collaborative filtering algorithm raises a lot in MAE, RMSE, recall, coverage and precision, and improves recommendation quality as well.关键词
推荐系统/协同过滤/组合相似度Key words
Recommendation system/Collaborative filtering/Combined similarity分类
信息技术与安全科学引用本文复制引用
查九,李振博,徐桂琼..基于组合相似度的优化协同过滤算法[J].计算机应用与软件,2014,(12):323-328,6.基金项目
国家自然科学基金项目（11201290）。 （）
