四川大学学报（自然科学版）2018，Vol.55Issue(1)：49-55,7.DOI:10.3969/j.issn.0490-6756.2018.01.009
基于改进协同过滤算法的个性化新闻推荐技术
Personalized news recommendation technology based on improved collaborative filtering algorithm
摘要
Abstract
The traditional collaborative filtering algorithm only based on matrix produced by user access history to make recommendation and sparse data ,and also cannot reflect the user's interests timely ,con-trary to these problems ,the personalized recommendation technology news in the traditional collabora-tive filtering algorithm proposes the calculation of news text content similarity and the concept of the time window ,the calculation of news content similarity also takes into account the part of speech and positions of the feature words in the news ,the time window is used to create user interest model which will change over time ;The experimental results show that the improved algorithm effectively improves the sparse problem of data which user has accessed and captures user interest timely ,F-measure value improves the maximum 11 .5％ compared to the traditional algorithm ,the highest value of mean absolute error fell by 8％ ,greatly improving the quality of recommendation .关键词
新闻推荐/协同过滤/内容相似度/时间窗Key words
News recommendation/Collaborative filtering/Connect similarity/Time window分类
信息技术与安全科学引用本文复制引用
黄贤英,熊李媛,李沁东..基于改进协同过滤算法的个性化新闻推荐技术[J].四川大学学报（自然科学版）,2018,55(1):49-55,7.基金项目
教育部人文社科青年基金项目(16YJC860010) （16YJC860010）
重庆市社会科学规划项目(2015BS059) （2015BS059）
国家自然科学基金项目(61603065) （61603065）
