  Algorithm selection using Metalearning aims to find mappings between problem
characteristics (i.e. metafeatures) with relative algorithm performance to
predict the best algorithm(s) for new datasets. Therefore, it is of the utmost
importance that the metafeatures used are informative. In Collaborative
Filtering, recent research has created an extensive collection of such
metafeatures. However, since these are created based on the practitioner's
understanding of the problem, they may not capture the most relevant aspects
necessary to properly characterize the problem. We propose to overcome this
problem by taking advantage of Representation Learning, which is able to create
an alternative problem characterizations by having the data guide the design of
the representation instead of the practitioner's opinion. Our hypothesis states
that such alternative representations can be used to replace standard
metafeatures, hence hence leading to a more robust approach to Metalearning. We
propose a novel procedure specially designed for Collaborative Filtering
algorithm selection. The procedure models Collaborative Filtering as graphs and
extracts distributed representations using graph2vec. Experimental results show
that the proposed procedure creates representations that are competitive with
state-of-the-art metafeatures, while requiring significantly less data and
without virtually any human input.
