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     author = {Ristoski, Petar and Loza Menc{\'{\i}}a, Eneldo and Paulheim, Heiko},
      month = may,
      title = {A Hybrid Multi-Strategy Recommender System Using Linked Open Data},
  booktitle = {Semantic Web Evaluation Challenge, Proceedings (ESWC 2014)},
     series = {Communications in Computer and Information Science},
     volume = {475},
       year = {2014},
      pages = {150-156},
  publisher = {Springer},
       isbn = {978-3-319-12023-2},
        url = {http://2014.eswc-conferences.org/sites/default/files/eswc2014-challenges_rs_submission_12.pdf},
        doi = {10.1007/978-3-319-12024-9_19},
   abstract = {In this paper, we discuss the development of a hybrid multi-strategy book recommendation system using Linked Open Data. Our approach 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 aggregation. We show that this approach delivers very good results in different recommendation settings and also allows for incorporating diversity of recommendations.}