Learning attribute-to-feature mappings for cold-start recommendations

Zeno Gantner, Lucas Drumond, Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme

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 higher-
dimensional) 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 attribute-
aware matrix factorization model for item recommendation
from implicit, positive-only feedback. Experiments on the new-
item problem show that this approach provides good predictive
accuracy, while the prediction time only grows by a constant
factor.