计算机应用与软件2017，Vol.34Issue(2)：285-289,312,6.DOI:10.3969/j.issn.1000-386x.2017.02.051
基于Logistic时间函数和用户特征的协同过滤算法
COLLABORATIVE FILTERING ALGORITHM BASED ON LOGISTIC TIME FUNCTION AND USER FEATURES
摘要
Abstract
At present,collaborative filtering algorithm is one of the most mature recommendation algorithms applied in recommendation systems.However,traditional collaborative filtering algorithms do not take into account the problem of users' interests drifting over time as well as the effects of feature attributes,which may decrease the accuracy of recommendation results.Hence,in order to enhance the traditional algorithms,a novel similarity measurement algorithm is put forward.In this paper,an innovative similarity measurement model is constructed by combining time-based Logistic weight function and user feature similarity-based data weight.Experimental results show that compared with traditional algorithms,the mean absolute error (MAE) of recommendation using the proposed algorithm is reduced by an average of 12％ and the quality of recommendation is improved significantly.关键词
协同过滤/兴趣变化/时间权重/用户特征Key words
Collaborative filtering/Interest change/Time weight/User feature分类
信息技术与安全科学引用本文复制引用
赵文涛,成亚飞,王春春..基于Logistic时间函数和用户特征的协同过滤算法[J].计算机应用与软件,2017,34(2):285-289,312,6.基金项目
河南省科技攻关项目(142402210435). （142402210435）
