TagRanker: learning to recommend ranked tags
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Logic Journal of IGPL 19 (2): 395-404 (2011)

In a social network, recommenders are highly demanded since they provide user interests in order to construct user profiles. This user profiles might be valuable to be exploited in business management or marketing, for instance. Basically, a tag recommender provides to users a set keywords that describe certain resources. The existing approaches require exploiting content information or they just provide a set of tags without any kind of preference order. This article proposes TagRanker, a tag recommender based on logistic regression that is free of exploiting content information. In addition, it gives a ranking of certain tags and learns just from the relations among users, resources and tags previously posted avoiding the cost of exploiting the content of the resources. An adequate evaluation measure for this specific kind of ranking is also proposed, since the existing ones just consider the tags as coming from a classification. The experiments on several data sets show that TagRanker can effectively recommend relevant tags outperforming the performance of a benchmark of Tag Recommender Systems.
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