An Ensemble Hypergraph Learning Framework for Recommendation

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 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.

Analysis of Hypergraph Signals via High-Order Total Variation

“…Hypergraph label learning has been widely applied to various scenarios such as automatic image annotation [43], visual classification of 2D or 3D objects [44][45][46], recommender systems [47,48], etc. Existing methods mostly utilize matrix-based or tensor-based forms to represent and process the HGS.…”