Abstract

In this paper we consider the problem of item recommendation in collaborative tagging communities, so called folksonomies, where users annotate interesting items with tags. Rather than following a collaborative filtering or annotation-based approach to recommendation, we extend the probabilistic latent semantic analysis (PLSA) approach and present a unified recommendation model which evolves from item user and item tag co-occurrences in parallel. The inclusion of tags reduces known collaborative filtering problems related to overfitting and allows for higher quality recommendations. Experimental results on a large snapshot of the delicious bookmarking service show the scalability of our approach and an improved recommendation quality compared to two-mode collaborative or annotation based methods.

@inproceedings{...,
author = {Robert Wetzker, Winfried Umbrath, Alan Said},
title = {A hybrid approach to item recommendation in folksonomies},
booktitle = {Proceedings of the Second ACM International Conference on Web Search and Data Mining WSDM 2009},
year = {2009},
isbn = {...},
pages = {...},
location = {Barcelona, Spain},
doi = {...},
publisher = {ACM},
address = {...},
}