document: Towards effective browsing of large scale social annotations This paper is concerned with the problem of browsing social annotations . Today , a lot of services ( e.g. , Del. icio . us , Filckr ) have been provided for helping users to manage and share their favorite URLs and photos based on social annotations . Due to the exponential increasing of the social annotations , more and more users , however , are facing the problem how to effectively find desired resources from large annotation data . Existing methods such as tag cloud and annotation matching work well only on small annotation sets . Thus , an effective approach for browsing large scale annotation sets and the associated resources is in great demand by both ordinary users and service providers . In this paper , we propose a novel algorithm , namely Effective Large Scale Annotation Browser ( ELSABer ) , to browse large-scale social annotation data . ELSABer helps the users browse huge number of annotations in a semantic , hierarchical and efficient way . More specifically , ELSABer has the following features : 1 ) the semantic relations between annotations are explored for browsing of similar resources ; 2 ) the hierarchical relations between annotations are constructed for browsing in a top-down fashion ; 3 ) the distribution of social annotations is studied for efficient browsing . By incorporating the personal and time information , ELSABer can be further extended for personalized and time-related browsing . A prototype system is implemented and shows promising results .
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