Collaborative Tag Recommendations

Abstract

With the increasing popularity of collaborative tagging systems, services that assist the user in the task of tagging, such as tag recommenders, are more and more required. Being the scenario similar to traditional recommender systems where nearest neighbor algorithms, better known as collaborative filtering, were extensively and successfully applied, the application of the same methods to the problem of tag recommendation seems to be a natural way to follow. However, it is necessary to take into consideration some particularities of these systems, such as the absence of ratings and the fact that two entity types in a rating scale correspond to three top level entity types, i.e., user, resources and tags. In this paper we cast the tag recommendation problem into a collaborative filtering perspective and starting from a view on the plain recommendation task without attributes, we make a ground evaluation comparing different tag recommender algorithms on real data.