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Cold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such cases, additional information about users (user attributes, e.g. gender, age, geographical location, occupation) and items (item attributes, e.g. genres, product categories, keywords) must be used. We describe a method that maps such entity (e.g. user or item) attributes to the latent features of a matrix (ordoi:10.1109/icdm.2010.129 dblp:conf/icdm/GantnerDFRS10 fatcat:zt42li26kjggxbyvu5ay4vpp64