Exploring Social Annotations with Application to Web Page Recommendation

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Exploring Social Annotations with Application to Web Page Recommendation
	Hui-Qian Li 1; Fen Xia 1; Daniel Zeng1,2 ; Fei-Yue Wang1 ; Wen-Ji Mao1
发表期刊	Journal of Computer Science and Technology
	2009-11
卷号	4 期号:6 页码:1028-1035
摘要	Collaborative social annotation systems allow users to record and share their original keywords or tag attachments to Web resources such as Web pages, photos, or videos. These annotations are a method for organizing and labeling information. They have the potential to help users navigate the Web and locate the needed resources. However, since annotations are posted by users under no central control, there exist problems such as spam and synonymous annotations. To efficiently use annotation information to facilitate knowledge discovery from the Web, it is advantageous if we organize social annotations from semantic perspective and embed them into algorithms for knowledge discovery. This inspires the Web page recommendation with annotations, in which users and Web pages are clustered so that semantically similar items can be related. In this paper we propose four graphic models which cluster users, Web pages and annotations and recommend Web pages for given users by assigning items to the right cluster first. The algorithms are then compared to the classical collaborative filtering recommendation method on a real-world data set. Our result indicates that the graphic models provide better recommendation performance and are robust to fit for the real applications.
关键词	Graphic Model Em (Expectation-maximization) Social Annotation Tag Recommendation
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12465
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Hui-Qian Li
作者单位	1.Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences Beijing 100190, China 2.Department of Management Information Systems, University of Arizona, Tucson AZ 85721, U.S.A.
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Hui-Qian Li,Fen Xia,Daniel Zeng,et al. Exploring Social Annotations with Application to Web Page Recommendation[J]. Journal of Computer Science and Technology,2009,4(6):1028-1035.
APA	Hui-Qian Li,Fen Xia,Daniel Zeng,Fei-Yue Wang,&Wen-Ji Mao.(2009).Exploring Social Annotations with Application to Web Page Recommendation.Journal of Computer Science and Technology,4(6),1028-1035.
MLA	Hui-Qian Li,et al."Exploring Social Annotations with Application to Web Page Recommendation".Journal of Computer Science and Technology 4.6(2009):1028-1035.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Exploring Social Ann（320KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载