  Cluster-based algorithm selection deals with selecting recommendation
algorithms on clusters of users to obtain performance gains. No studies have
been attempted for many combinations of clustering approaches and
recommendation algorithms. We want to show that clustering users prior to
algorithm selection increases the performance of recommendation algorithms. Our
study covers eight datasets, four clustering approaches, and eight
recommendation algorithms. We select the best performing recommendation
algorithm for each cluster. Our work shows that cluster-based algorithm
selection is an effective technique for optimizing recommendation algorithm
performance. For five out of eight datasets, we report an increase in nDCG@10
between 19.28% (0.032) and 360.38% (0.191) compared to algorithm selection
without prior clustering.
