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A Hybrid Approach to Item Recommendation in Folksonomies

, , and . Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval, page 25--29. New York, NY, USA, ACM, (2009)
DOI: 10.1145/1506250.1506255

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.

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A hybrid approach to item recommendation in folksonomies

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