计算机应用与软件2018，Vol.35Issue(5)：307-312,6.DOI:10.3969/j.issn.1000-386x.2018.05.055
基于加权融合的E2LSH用户相似度计算
E2LSH USER SIMILARITY CALCULATION BASED ON WEIGHTED FUSION
陈浩 1马娅婕 1金瑾 1徐高凯1
作者信息
- 1. 武汉科技大学信息科学与工程学院 湖北武汉430081
- 折叠
摘要
Abstract
When the traditional collaborative filtering algorithm encounters the massive, high-dimensionality of the real scene,it is difficult to directly calculate the user similarity and the real-time performance is poor.This paper used the local sensitive hash(E2LSH)algorithm based on p-stable distribution to calculate the similarity between users after dimension reduction of massive high-dimensional data.Based on the accuracy of the similarity calculation,we used the model fusion technology to weight the similar users calculated by multiple E 2LSH algorithms to get the most similar users.At the same time, for users with higher similarity, we used a weighted average method to target users to score predictions for uninteractive products and to sort products for recommendation, thereby improving the real-time and accuracy of recommendations.The experimental results show that the proposed algorithm had greatly improved the similarity calculation and recommendation accuracy of users.关键词
局部敏感哈希/模型融合/协同过滤/加权平均Key words
E2LSH/Model fusion/Collaborative filtering/Weighted average分类
信息技术与安全科学引用本文复制引用
陈浩,马娅婕,金瑾,徐高凯..基于加权融合的E2LSH用户相似度计算[J].计算机应用与软件,2018,35(5):307-312,6.
