Personalized Collaborative Tagging
Social media systems have encouraged end user participation in the
Internet, for the purpose of storing and distributing Internet
content, sharing opinions and maintaining
relationships. Collaborative tagging allows users to annotate the
resulting user-generated content, and enables effective retrieval of
otherwise uncategorized data. However, compared to professional web
content production, collaborative tagging systems face the challenge
that end-users assign tags in an uncontrolled manner, resulting in
unsystematic and inconsistent metadata.
This paper introduces a framework for the personalization of social
media systems. We pinpoint three tasks that would benefit from
personalization: collaborative tagging, collaborative
browsing and collaborative search. We propose a ranking
model for each task that integrates the individual user’s tagging
history in the recommendation of tags and content, to align its
suggestions to the individual user preferences. We demonstrate on
two real data sets that for all three tasks, the personalized
ranking should take into account both the user’s own preference and
the opinion of others.
@article{Wang:tagging2009,
author = {J. Wang and M. Clements and J. Yang and and A.P. de Vries and M.J.T. Reinders},
title = {Personalized Collaborative Tagging},
journal = {Information Processing & Management},
year = {2009},
}
