  There are many offline metrics that can be used as a reference for evaluation
and optimization of the performance of recommender systems. Hybrid
recommendation approaches are commonly used to improve some of those metrics by
combining different systems. In this work we focus on music recommendation and
propose a new way to improve recommendations, with respect to a desired metric
of choice, by combining multiple systems for each user individually based on
their expected performance. Essentially, our approach consists in predicting an
expected error that each system will produce for each user based on their
previous activity. To this end, we propose to train regression models for
different metrics predicting the performance of each system based on a number
of features characterizing previous user behavior in the system. We then use
different fusion strategies to combine recommendations generated by each
system. Following this approach one can optimize the final hybrid system with
respect to the desired metric of choice. As a proof of concept, we conduct
experiments combining two recommendation systems, a Matrix Factorization model
and a popularity-based recommender. We use the data provided by Melon, a Korean
music streaming service, to train and evaluate the performance of the systems.
