A hybrid PLSA approach for warmer cold start in folksonomy recommendation

AutorAlan Said, Robert Wetzker, Winfried Umbrath, Leonhard Hennig
QuelleProceedings of the RecSys'09 Workshop on Recommender Systems & The Social Web, New York, NY 
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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.