摘要
单类个性化协同排序算法的研究的核心思想是把单类协同过滤问题当成排序问题来看待。之前的研究仅仅使用了隐式反馈数据来对推荐对象进行排序,这限制了推荐的准确度。随着在线社交网络的出现,为了进一步提高单类个性化协同排序算法的准确度,提出了一种新的融合社交网络的单类个性化协同排序算法。在真实的包含社交网络的2个数据集上的实验验证了该算法在各个评价指标下的性能均优于几个经典的单类协同过滤算法。实验证明,社交网络信息对于提高单类个性化协同排序算法的性能具有重要作用。
The research＇s key idea of one-class personalized collaborative Ranking Algorithm is to make use of partial order of items.In the early research of these problems,the training data are only implicit feedback dataset,this limits the sorting accuracy.With the advent of online social networks,in order to improve the performance of one-class personalized collaborative ranking algorithm,we proposed a new one-class personalized collaborative ranking algorithm incorporating social network.We conducted our experiment on two large real-world datasets with social information.The experiment results illustrate that our approach achieves a better performance than several traditional OCCF methods.Experiments also show that the social network information plays an important role in improving the performance of one-class personalized collaborative ranking algorithm.
作者
李改
陈强
李磊
潘进财
LI Gai CHEN Qiang LI Lei PAN Jin-cai(Department of Electronic and Informat ion Engineering, Shunde Polytechnic, Shunde 528333, China School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510006, China Department of Computer Science, Guangdong University of Education, Guangzhou 510303, China)
出处
《计算机科学》
CSCD
北大核心
2017年第2期88-92,116,共6页
Computer Science
基金
国家自然科学基金项目(61370186)
广东省自然科学基金项目(2016A030310018)
广东省科技计划项目(2014A010103040
2014B010116001)
广州市科技计划项目(201604010049
201510010203)
广东第二师范学院教授博士科研专项(2015ARF25)
佛山市机电专业群工程技术开发中心2015年第二批开放课题(2015-KJZX139)
广东省大学生科技创新培育专项(G2016Z08)资助
关键词
推荐系统
协同排序
社交网络
单类协同过滤
隐式反馈数据
Recommended systems
Collaborative ranking
Social network
One-class collaborative filtering
Implicit feedback dataset
