计算机技术与发展2016，Vol.26Issue(3)：63-66,4.DOI:10.3969/j.issn.1673-629X.2016.03.015
改进的面向数据稀疏的协同过滤推荐算法
An Improved Collaborative Filtering Recommendation Algorithm for Data Sparsity
摘要
Abstract
User similarity and nearest neighbor set is two important steps in acollaborative filtering algorithm. The traditional Collaborative Filtering ( CF) computes user similarity only relying on user rating and finds K neighbors as nearest neighbor to produce recommendation for users,but in the case of sparse data,only relying on user rating calculation makes the recommendation effect inaccurate. To solve the problems,an improved collaborative filtering recommendation algorithm for data sparsity is proposed,which introduces the similarity of user attributes and user interest,combined with traditional user rating similarity to compute similarity between users. The weights of three is adjusted through several experiments,and the dynamic method is used to search the user’ s nearest neighbor to recommend suitable i-tems for users,in order to alleviate user data sparsity problem. Experimental results show that this method can make full use of all kinds of users’ data information,improving the accuracy of predicted ratings and quality of recommendation.关键词
用户相似性/属性/兴趣/动态/数据稀疏性Key words
user similarity/attribute/interest/dynamic/data sparsity分类
信息技术与安全科学引用本文复制引用
高倩,何聚厚..改进的面向数据稀疏的协同过滤推荐算法[J].计算机技术与发展,2016,26(3):63-66,4.基金项目
中央高校基本科研业务费专项资金资助项目(GK201002028,GK201101001) （GK201002028,GK201101001）
陕西师范大学学习科学交叉学科培育计划资助项目 （）
