In this paper, we discuss the development of a hybrid multi- strategy book recommendation system using Linked Open Data. Our ap- proach builds on training individual base recommenders and using global popularity scores as generic recommenders. The results of the individual recommenders are combined using stacking regression and rank aggrega- tion. We show that this approach delivers very good results in dierent recommendation settings and also allows for incorporating diversity of recommendations.
['Petar Ristoski', 'Eneldo Loza Mencía', 'Heiko Paulheim']
A Hybrid Multi-strategy Recommender System Using Linked Open Data