WI 10
Language Models & Topic Models for Personalizing Tag Recommendation
Abstract
More and more content on the Web is generated by users. To organize this
information and make it accessible via current search technology, tagging
systems have gained tremendous popularity. Especially for multimedia content
they allow to annotate resources with keywords (tags) which opens the door for
classic text-based information retrieval. To support the user in choosing
the right keywords, tag recommendation algorithms have emerged. In this
setting, not only the content is decisive for recommending relevant tags but
also the user's preferences.
In this paper we introduce an approach to personalized tag recommendation
that combines a probabilistic model of tags from the resource with tags
from the user. As models we investigate simple language models as well as Latent
Dirichlet Allocation. Extensive experiments on a real world dataset crawled
from a big tagging system show that personalization improves tag
recommendation, and our approach significantly outperforms state-of-the-art
approaches.
Full Paper
WI10.pdf
Conference Homepage
WI-IAT 2010
BibTex Entry
