Yhdistelmäsuosittelijat
Yhdistelmäsuosittelijat, or ensemble recommenders, are a type of recommender system that combines multiple individual recommendation algorithms to produce a final, often improved, set of recommendations. The core idea is that by leveraging the strengths of different recommenders and mitigating their weaknesses, a more robust and accurate recommendation can be achieved. This approach is analogous to ensemble methods in machine learning, such as random forests or gradient boosting, where multiple models are trained and their predictions are aggregated.
The process of building an ensemble recommender typically involves several steps. First, a diverse set of base
The benefits of using ensemble recommenders often include increased accuracy and robustness. By combining diverse sources