A hybrid approach to item recommendation in folksonomies


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.

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 = {...},
Robert Wetzker, Winfried Umbrath, Alan Said
ESAIR 2009 : Exploiting Semantic Annotations in Information Retrieval