A hybrid approach to item recommendation in folksonomies
R. Wetzker, W. Umbrath, и A. Said. ESAIR '09: Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval, стр. 25--29. New York, NY, USA, ACM, (2009)
Аннотация
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.
Описание
A hybrid approach to item recommendation in folksonomies
