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.

翻译：推荐系统旨在预测用户对物品集合的偏好。这些系统通过处理用户的历史交互，决定哪些物品应获得更高排名以满足用户需求。集成推荐系统能够通过有效融合单个模型生成的决策实现卓越的推荐性能。本文提出一种新型集成推荐系统，将不同模型的预测结果整合到统一的超图排序框架中。这是首次将超图排序应用于对集成推荐系统进行建模。超图是图的泛化形式，其中多个顶点可通过超边连接，从而高效建模高阶关系。我们通过为单个推荐系统分配不同的超边权重，区分用户与物品之间的真实连接和预测连接。我们使用来自电影、音乐和新闻媒体推荐领域的四个数据集进行实验。结果表明，相较于单个模型及加权混合方法，集成超图排序方法能生成更精确的推荐。与设置相同超边权重的方案相比，为集成超图分配差异化超边权重可进一步提升性能。