  Multi-criteria recommender systems can improve the quality of recommendations
by considering user preferences on multiple criteria. One promising approach
proposed recently is multi-criteria ranking, which uses Pareto ranking to
assign a ranking score based on the dominance relationship between predicted
ratings across criteria. However, applying Pareto ranking to all criteria may
result in non-differentiable ranking scores. To alleviate this issue, we
proposed a hybrid multi-criteria ranking method by using subsorting. More
specifically, we utilize one ranking method as the major sorting approach,
while we apply another preference ordering method as subsorting. Our
experimental results on the OpenTable and Yahoo!Movies data present the
advantages of this hybrid ranking approach. In addition, the experiments also
reveal more insights about the sustainability of the multi-criteria ranking for
top-N item recommendations.
