计算机工程与应用2019，Vol.55Issue(4)：225-232,8.DOI:10.3778/j.issn.1002-8331.1711-0063
面向个性化网站的增量协同过滤推荐方法
Incremental Collaborative Filtering Recommendation Method for Personalized Websites
摘要
Abstract
In order to solve the problems that user’s retrieval intention is rarely considered in the personalized websites and the search result is not good, this paper proposes an Incremental Collaborative Filtering Recommendation method (ICFR). The ICFR model improves the collaborative filtering recommendation algorithm, which is one of the most popular recommendation algorithms, and applies it to the personalized websites. Firstly, the browsing behavior information of users is extracted by analyzing web logs and normalized into rating value. Secondly, the similarity value between users is obtained by using the improved similarity calculation method, and the nearest neighbor set which can reflect the user’s intention is selected. Thirdly, the results predicted by the nearest neighbor set are sorted and returned to the user as the rec-ommended list. Finally, historical user preference data are updated effectively in real time by the incremental updating algorithm. The experimental results indicate that the incremental collaborative filtering recommendation model is suitable for personalized websites and this method can make the retrieval results reflect the user intention.关键词
个性化网站/基于用户的协同过滤算法/推荐系统/用户意图/增量式更新Key words
personalized website/ user-based collaborative filtering/ recommendation system/ user intention/ incremental updating分类
信息技术与安全科学引用本文复制引用
李婷,张瑞芳,郭克华..面向个性化网站的增量协同过滤推荐方法[J].计算机工程与应用,2019,55(4):225-232,8.基金项目
国家自然科学基金(No.61202341) （No.61202341）
高维信息智能感知与系统教育部重点实验室创新基金(No.JYB201502) （No.JYB201502）
科技部国家国际科技合作专项项目(No.2013DFB10070) （No.2013DFB10070）
湖南省创新平台专项(No.2012GK4106) （No.2012GK4106）
中南大学中央高校基本科研业务费专项资金(No.2017zzts718). （No.2017zzts718）
