HypeRS: Building a Hypergraph-driven ensemble Recommender System
This addresses the need for better recommendation systems in domains like movies, music, and news by introducing hypergraph ranking to ensemble methods, though it appears incremental as it builds on existing ensemble techniques.
The paper tackles the problem of improving recommendation accuracy by proposing HypeRS, a novel ensemble recommender system that combines predictions from multiple models using a hypergraph ranking framework, which achieved more accurate recommendations than individual models and a weighted hybrid approach across four datasets.
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.