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One of the most challenging recommendation tasks is recommending to a new,previously unseen user. This is known as the 'user cold start' problem.Assuming certain features or attributes of users are known, one approach forhandling new users is to initially model them based on their features.  Motivated by an ad targeting application, this paper describes an extremeonline recommendation setting where the cold start problem is perpetual. Everyuser is encountered by the system just once, receives a recommendation, andeither consumes or ignores it, registering a binary reward.  We introduce One-pass Factorization of Feature Sets, OFF-Set, a novelrecommendation algorithm based on Latent Factor analysis, which models users bymapping their features to a latent space. Furthermore, OFF-Set is able to modelnon-linear interactions between pairs of features. OFF-Set is designed forpurely online recommendation, performing lightweight updates of its model pereach recommendation-reward observation. We evaluate OFF-Set against severalstate of the art baselines, and demonstrate its superiority on realad-targeting data.

OFF-Set: One-pass Factorization of Feature Sets for Online Recommendation in Persistent Cold Start Settings