  Recommender systems are designed to predict user preferences over collections
of items. These systems process users' previous interactions to decide which
items should be ranked higher to satisfy their desires. An ensemble recommender
system can achieve great recommendation performance by effectively combining
the decisions generated by individual models. In this paper, we propose a novel
ensemble recommender system that combines predictions made by different models
into a unified hypergraph ranking framework. This is the first time that
hypergraph ranking has been employed to model an ensemble of recommender
systems. Hypergraphs are generalizations of graphs where multiple vertices can
be connected via hyperedges, efficiently modeling high-order relations. We
differentiate real and predicted connections between users and items by
assigning different hyperedge weights to individual recommender systems. We
perform experiments using four datasets from the fields of movie, music and
news media recommendation. The obtained results show that the ensemble
hypergraph ranking method generates more accurate recommendations compared to
the individual models and a weighted hybrid approach. The assignment of
different hyperedge weights to the ensemble hypergraph further improves the
performance compared to a setting with identical hyperedge weights.
