Abstract-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 (or higherdimensional) factorization model. With such mappings, the factors of a MF model trained by standard techniques can be applied to the new-user and the new-item problem, while retaining its advantages, in particular speed and predictive accuracy. We use the mapping concept to construct an attributeaware matrix factorization model for item recommendation from implicit, positive-only feedback. Experiments on the newitem problem show that this approach provides good predictive accuracy, while the prediction time only grows by a constant factor.
Their mapping technique was utilized to generate an attribute aware MF model for item recommendation from implicit, positive-only feedback REF .
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations
2010 IEEE International Conference on Data Mining
2010 IEEE International Conference on Data Mining
Computer Science