计算机工程2017，Vol.43Issue(2)：74-78,5.DOI:10.3969/j.issn.1000-3428.2017.02.013
基于精确欧氏局部敏感哈希的改进协同过滤推荐算法
Improved Collaborative Filtering Recommendation Algorithm Based on Exact Euclidean Locality Sensitive Hashing
钟川 1陈军1
作者信息
- 1. 武汉大学国家多媒体软件工程技术研究中心,武汉430072
- 折叠
摘要
Abstract
Aiming at the large scale and high sparsity degree of user rating data and poor real-time capability of direct similarity calculation,this paper proposes a layered Exact Euclidean Locality Sensitive Hashing(E2LSH) algorithm based on p-stable distribution.It finds similar users to improve computing efficiency by using E2LSH algorithm,and uses weighted mean method to predict score for not rated items to improve the accuracy of recommendation results after getting the similar users.Experimental results show that,compared with the collaborative filtering recommendation algorithm based on Locality Sensitive Hashing (LSH),this algorithm has higher efficiency and recommendation accuracy.关键词
精确欧氏局部敏感哈希/相似度/排序/协同过滤/推荐系统Key words
Exact Euclidean Locality Sensitive Hashing(E2LSH)/similarity/sort/collaborative filtering/recommendation system分类
信息技术与安全科学引用本文复制引用
钟川,陈军..基于精确欧氏局部敏感哈希的改进协同过滤推荐算法[J].计算机工程,2017,43(2):74-78,5.
