Random Walks on Context-Aware Relation Graphs for Ranking Social Tags

Social tagging provides an efficient way to manage online resources. In order to collect more social tags, many research efforts aim to automatically suggest tags to help users annotate tags. Many content-based methods assume tags are independent and suggest tags one by one independently. Although it makes suggestion easier, the independence assumption does not confirm to reality, and the suggested tags are usually inconsistent and incoherent with each other. To address this problem, we propose to model contextaware relations of tags for suggestion: (1) By regarding resource content as context of tags, we propose Tag Context Model to identify specific context words in resource content for tags. (2) Given a new resource, we build a context-aware relation graph of candidate tags, and propose a random walk algorithm to rank tags for suggestion. Experiment results demonstrate ��� � ��������������������������������������� our method outperforms other state-of-the-art methods