Ensemble Boost: Greedy Selection for Superior Recommender Systems

TL;DR

This paper introduces a greedy ensemble selection method that combines multiple recommendation models to significantly improve recommendation accuracy across various datasets, outperforming single models and existing ensemble techniques.

Contribution

It presents a novel greedy ensemble selection approach for recommender systems, demonstrating its effectiveness through extensive experiments and outperforming existing methods.

Findings

Average NDCG improvement of 21.67% across datasets

Significant accuracy gains over single best models

Effective combination of diverse recommendation strategies

Abstract

Ensemble techniques have demonstrated remarkable success in improving predictive performance across various domains by aggregating predictions from multiple models [1]. In the realm of recommender systems, this research explores the application of ensemble technique to enhance recommendation quality. Specifically, we propose a novel approach to combine top-k recommendations from ten diverse recommendation models resulting in superior top-n recommendations using this novel ensemble technique. Our method leverages a Greedy Ensemble Selection(GES) strategy, effectively harnessing the collective intelligence of multiple models. We conduct experiments on five distinct datasets to evaluate the effectiveness of our approach. Evaluation across five folds using the NDCG metric reveals significant improvements in recommendation accuracy across all datasets compared to single best performing model. Furthermore, comprehensive comparisons against existing models underscore the efficacy of our ensemble approach in enhancing recommendation quality. Our ensemble approach yielded an average improvement of 21.67% across different NDCG@N metrics and the five datasets, compared to single best model. The popularity recommendation model serves as the baseline for comparison. This research contributes to the advancement of ensemble-based recommender systems, offering insights into the potential of combining diverse recommendation strategies to enhance user experience and satisfaction. By presenting a novel approach and demonstrating its superiority over existing methods, we aim to inspire further exploration and innovation in this domain.