  Collaborative Filtering (CF) has become the standard approach to solve
recommendation systems (RS) problems. Collaborative Filtering algorithms try to
make predictions about interests of a user by collecting the personal interests
from multiple users. There are multiple CF algorithms, each one of them with
its own biases. It is the Machine Learning practitioner that has to choose the
best algorithm for each task beforehand. In Recommender Systems, different
algorithms have different performance for different users within the same
dataset. Meta Learning (MtL) has been used to choose the best algorithm for a
given problem. Meta Learning is usually applied to select algorithms for a
whole dataset. Adapting it to select the to the algorithm for a single user in
a RS involves several challenges. The most important is the design of the
metafeatures which, in typical meta learning, characterize datasets while here,
they must characterize a single user. This work presents a new meta-learning
based framework named $\mu$-cf2vec to select the best algorithm for each user.
We propose using Representation Learning techniques to extract the
metafeatures. Representation Learning tries to extract representations that can
be reused in other learning tasks. In this work we also implement the framework
using different RL techniques to evaluate which one can be more useful to solve
this task. In the meta level, the meta learning model will use the metafeatures
to extract knowledge that will be used to predict the best algorithm for each
user. We evaluated an implementation of this framework using MovieLens 20M
dataset. Our implementation achieved consistent gains in the meta level,
however, in the base level we only achieved marginal gains.
