Towards ensemble learning for hybrid music recommendation
Marco Tiemann, Steffen C. Pauws · 2007
We investigate ensemble learning methods for hybrid music recommender algorithms, combining a social and a content-based recommender algorithm as weak learners by applying a combination rule to unify the weak learners' output. A first experiment suggests that such a combination can already reduce the mean absolute prediction error compared to the weak learners' individual errors.