A hybrid PLSA approach for warmer cold start in folksonomy recommendation
msra(2009)
摘要
We investigate the problem of item recommendation during the first months of the collaborative tagging community Ci teULike. CiteULike is a so-called folksonomy where users have the possibility to organize publications through anno tations - tags. Making reliable recommendations during the initial phase of a folksonomy is a difficult task, since infor mation about user preferences is meager. In order to im prove recommendation results during this cold start period, we present a probabilistic approach to item recommenda tion. Our model extends previously proposed models such as probabilistic latent semantic analysis (PLSA) by merging both user-item as well as item-tag observations into a unified representation. We find that bringing tags into play reduces the risk of overfitting and increases overall recommendation quality. Experiments show that our approach outperforms other types of recommenders.
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关键词
citeulike,cold start,recommendations,folksonomies,plsa
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