A Combined Method for Mitigating Sparsity Problem in Tag Recommendation

Tag recommendation is a specific recommendation task for recommending metadata (tag) for a web resource (item) during user annotation process. In this context, sparsity problem refers to situation where tags need to be produced for items with few annotations or for user who tags few items. Most of the state of the art approaches in tag recommendation are rarely evaluated or perform poorly under this situation. This paper presents a combined method for mitigating sparsity problem in tag recommendation by mainly expanding and ranking candidate tags based on similar items' tag and existing tag ontology. We evaluated the approach on two public social book marking datasets. The experiment results show better accuracy for recommendation in sparsity situation over several state of the art methods.

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