  To select the best algorithm for a new problem is an expensive and difficult
task. However, there are automatic solutions to address this problem: using
Metalearning, which takes advantage of problem characteristics (i.e.
metafeatures), one is able to predict the relative performance of algorithms.
In the Collaborative Filtering scope, recent works have proposed diverse
metafeatures describing several dimensions of this problem. Despite interesting
and effective findings, it is still unknown whether these are the most
effective metafeatures. Hence, this work proposes a new set of graph
metafeatures, which approach the Collaborative Filtering problem from a Graph
Theory perspective. Furthermore, in order to understand whether metafeatures
from multiple dimensions are a better fit, we investigate the effects of
comprehensive metafeatures. These metafeatures are a selection of the best
metafeatures from all existing Collaborative Filtering metafeatures. The impact
of the most representative metafeatures is investigated in a controlled
experimental setup. Another contribution we present is the use of a
Pareto-Efficient ranking procedure to create multicriteria metatargets. These
new rankings of algorithms, which take into account multiple evaluation
measures, allow to explore the algorithm selection problem in a fairer and more
detailed way. According to the experimental results, the graph metafeatures are
a good alternative to related work metafeatures. However, the results have
shown that the feature selection procedure used to create the comprehensive
metafeatures is is not effective, since there is no gain in predictive
performance. Finally, an extensive metaknowledge analysis was conducted to
identify the most influential metafeatures.
