personalized recommendation using implicit interaction information

ISCAS OpenIR

	personalized recommendation using implicit interaction information
	Nancheng Liu; Jiang Qingshan; Chen HaiShan; Wang Beizhan
	2011
会议名称	6th International Conference on Computer Science and Education, ICCSE 2011
会议录名称	ICCSE 2011 - 6th International Conference on Computer Science and Education, Final Program and Proceedings
页码	1340-1345
会议日期	August 3, 2011 - August 5, 2011
会议地点	Singapore, Singapore
收录类别	EI
ISBN	9781424497188
部门归属	(1) Software School Xiamen University Xiamen China; (2) Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China
摘要	Currently, the information in the internet is becoming explosive. In order to help the users searching the items they are interested in, such as, the news, the books, in this paper, we propose an automatic personalized recommendation algorithm by constructing the social graph resting on the users' implicit interaction information. We at first introduce a metric to measure the users' affinity based on their implicit interaction information to construct a social graph, and then categorize the users into different clusters within which they will have similar tastes, finally, we use a personalized recommendation algorithm to recommend the items shared in the same cluster to the users. The experiments on a book data set are performed to demonstrate that our proposed method can well generate the recommendations which users will be interested in with high accuracy and efficiency. © 2011 IEEE.; Currently, the information in the internet is becoming explosive. In order to help the users searching the items they are interested in, such as, the news, the books, in this paper, we propose an automatic personalized recommendation algorithm by constructing the social graph resting on the users' implicit interaction information. We at first introduce a metric to measure the users' affinity based on their implicit interaction information to construct a social graph, and then categorize the users into different clusters within which they will have similar tastes, finally, we use a personalized recommendation algorithm to recommend the items shared in the same cluster to the users. The experiments on a book data set are performed to demonstrate that our proposed method can well generate the recommendations which users will be interested in with high accuracy and efficiency. © 2011 IEEE.
关键词	Algorithms Computer Science Education Computing
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16308
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Nancheng Liu,Jiang Qingshan,Chen HaiShan,et al. personalized recommendation using implicit interaction information[C],2011:1340-1345.