摘要
近年来很多基于协同过滤的推荐系统得到了成功应用,但随着系统中用户和项目数量的不断增加,相似度计算量剧增,使得协同过滤推荐系统的扩展性问题变得日益突出。本文提出改进的基于近似最近邻的LSH/MinHash算法,并运用到图书馆资源聚类中,以解决在合理时间复杂度下的高维大数据量聚类问题,降低相似度计算量,提高算法的可扩展性。实验表明此算法有较高的效率与精度。
In recent years , many collaborative filtering-based recommender systems have been successfully applied , but with the increasing number of system users and projects , the amount of similarity calculation increases sharply , collaborative filtering rec-ommendation system scalability issues become increasingly prominent .This paper puts forward a LSH/MinHash algorithm based on the approximate nearest neighbor , and applies it to the clustering of library resources , for solving the problem of high dimen-sion and a amount of data cluster in the complexity under reasonable time .It reduces the amount of similarity calculation , im-proves the scalability of the algorithm .Experiments show that this algorithm is of higher efficiency and accuracy .
出处
《计算机与现代化》
2013年第12期19-22,26,共5页
Computer and Modernization
关键词
图书馆
个性化推荐
协同过滤
LSH
library
personalized recommendation
collaborative filtering
LSH
