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.

翻译：多准则推荐系统通过考虑用户对多个准则的偏好，能够提升推荐质量。近期提出的一种有前景的方法是采用帕累托排序（Pareto ranking），根据各准则预测评分间的支配关系分配排序分数。然而，将帕累托排序应用于所有准则可能导致不可微的排序分数。为缓解这一问题，我们提出了一种基于子排序的混合多准则排序方法。具体而言，该方法将一种排序方法作为主排序方式，同时应用另一种偏好排序方法作为子排序。基于OpenTable和Yahoo!Movies数据集的实验结果表明，这种混合排序方法具有显著优势。此外，实验还揭示了多准则排序在Top-N项目推荐中的可持续性特征。