A Graph-Coarsening Approach for Tag Recommendation

In this paper we propose a new graph-based tag recommendation approach. The approach is structured into an offline step and an online one. Offline, the hypergraph depicting the history of tags assignment by users to resources is abstracted. On online, for a given target user and a resource, we first compute the set of recommended abstract tags (i.e tag clusters) applying a basic graph-based approach to the abstract graph. A new reduced graph is computed by unfolding the abstract subgraph composed of the set of recommended abstract tags and nodes representing the cluster of users (resp. resources) to which the target user (resp. resource) belongs to. Again the same basic graph-based tag recommendation approach is applied to this new reduced graph in order to compute the final set of tags to recommend. Experiments on real dataset show the effectiveness of the proposed approach.