Abstract

Weinvestigatetheproblem ofitemrecommendationduring the first months of the collaborative tagging community CiteULike. CiteULike is a so-called folksonomy where users have the possibility to organize publications through annotations -tags. Making reliable recommendations during the initial phase of a folksonomy is a difficult task, since information about user preferences is meager. In order to improve recommendation results during this cold start period, we present a probabilistic approach to item recommendation. Our model extends previously proposed models such asprobabilisticlatent semantic analysis(PLSA)by merging bothuser-itemaswell asitem-tagobservationsintoaunified representation. We find thatbringing tagsintoplay reduces the risk of overfitting and increases overall recommendation quality. Experiments show that our approach outperforms other types of recommenders.

@inproceedings{said09a,
booktitle = {Proceedings of the RecSys'09 Workshop on Recommender Systems & The Social Web},
author = {Alan Said and Robert Wetzker and Winfried Umbrath and Leonhard Hennig},
title = {A hybrid PLSA approach for warmer cold start in folksonomy recommendation},
year = {2009},
location = {New York, NY, USA},
publisher={CEUR-WS Vol. 532},
pages={87-90}
}