Track: Web Engineering
Towards Effective Browsing of Large Scale Social Annotations
- Rui Li (Shanghai Jiao Tong University)
- Shenghua Bao (Shanghai Jiao Tong University)
- Ben Fei (IBM China Research Lab)
- Zhong Su (IBM China Research Lab)
- Yong Yu (Shanghai Jiao Tong University)
This paper is concerned with the problem of browsing social annotations. Today, a lot of services (e.g., Del.icio.us, Flickr) have been provided based on social annotations. These services help users to manage and share their favorite URL, photos etc. However, due to exponential increasing of the social annotations, more and more users are facing the problem of finding desired resources among a large annotation data. Existing methods such as tag cloud, annotation matching, work well only when the annotation scale is relatively small. Thus, an effective approach for browsing the large scale annotations and the associated resources is on great demand of both users and service providers. In this paper, we propose a novel algorithm, namely Effective Large Scale Annotation Browser (ELSABer), to browse the large-scale social annotations. With the help of ELSABer, the users could browse the large-sale annotations in a semantic, hierarchical and efficient way. More specifically, 1) the semantic relations among annotations are explored for similar resource browsing; 2) the hierarchical relations among annotations are also constructed for top-down browsing; 3) the power-law distribution of social annotations is studied for efficient browsing. By incorporating the personal and time information, the ELSABer can be further extended for personalized and time-related browsing. A prototype system is also implemented based on ELSABer and shows promising results.