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
%0 Journal Article
%1 marinho2007collaborative
%A Marinho, Leandro Balby
%A Schmidt-Thieme, Lars
%D 2007
%K collaborative tag tag_recommendation
%T Collaborative Tag Recommendations
%X 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
@article{marinho2007collaborative,
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},
added-at = {2014-02-28T07:39:12.000+0100},
author = {Marinho, Leandro Balby and Schmidt-Thieme, Lars},
biburl = {https://www.bibsonomy.org/bibtex/2dc5a58efeb57de00610210e56660ffb2/inmantang},
interhash = {65cc2db164fe897f5073b2e05cecdae2},
intrahash = {dc5a58efeb57de00610210e56660ffb2},
keywords = {collaborative tag tag_recommendation},
timestamp = {2014-02-28T07:39:12.000+0100},
title = {Collaborative Tag Recommendations},
year = 2007
}