HypeRS: Building a Hypergraph-driven ensemble Recommender System
Alireza Gharahighehi, Celine Vens, Konstantinos Pliakos

TL;DR
HypeRS introduces a novel hypergraph-based ensemble recommender system that models high-order relations among users and items, significantly improving recommendation accuracy over individual models and traditional hybrid methods.
Contribution
This paper pioneers the use of hypergraph ranking for ensemble recommender systems, effectively combining multiple models with weighted hyperedges to enhance recommendation performance.
Findings
Hypergraph ensemble outperforms individual models.
Weighted hyperedges improve recommendation accuracy.
Method effective across multiple datasets.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsRecommender Systems and Techniques · Advanced Graph Neural Networks · Complex Network Analysis Techniques
