  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.
