An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Functional matrix factorizations for cold-start recommendation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Factorization Machines with libFM
ACM Transactions on Intelligent Systems and Technology (TIST)
Build your own music recommender by modeling internet radio streams
Proceedings of the 21st international conference on World Wide Web
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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 for handling new users is to initially model them based on their features. Motivated by an ad targeting application, this paper describes an extreme online recommendation setting where the cold start problem is perpetual. Every user is encountered by the system just once, receives a recommendation, and either consumes or ignores it, registering a binary reward. We introduce One-pass Factorization of Feature Sets, 'OFF-Set', a novel recommendation algorithm based on Latent Factor analysis, which models users by mapping their features to a latent space. OFF-Set is able to model non-linear interactions between pairs of features, and updates its model per each recommendation-reward observation in a pure online fashion. We evaluate OFF-Set against several state of the art baselines, and demonstrate its superiority on real ad-targeting data.