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.

翻译：集成技术通过聚合多个模型的预测结果，已在多个领域展现出显著提升预测性能的能力[1]。在推荐系统领域，本研究探索了应用集成技术以提升推荐质量的方法。具体而言，我们提出了一种新颖的方法，通过整合来自十个不同推荐模型的top-k推荐结果，利用这种创新的集成技术生成更优的top-n推荐。我们的方法采用贪心集成选择策略，有效利用了多个模型的集体智能。我们在五个不同的数据集上进行了实验以评估方法的有效性。使用NDCG指标进行的五折交叉验证表明，与单一最佳模型相比，所有数据集的推荐准确率均获得显著提升。此外，与现有模型的全面对比进一步证实了我们集成方法在提升推荐质量方面的有效性。相较于单一最佳模型，我们的集成方法在不同NDCG@N指标及五个数据集上平均实现了21.67%的性能提升。流行度推荐模型被用作比较基准。本研究推动了基于集成的推荐系统的发展，为融合多样化推荐策略以提升用户体验和满意度提供了新的见解。通过提出创新方法并证明其相对于现有方法的优越性，我们期望激发该领域的进一步探索与创新。