Abstract
This paper provides an overview of the work done in the ESWC Linked Open Data-enabled Recommender Systems challenge, in which we proposed an ensemble of algorithms based on popularity, Vector Space Model, Random Forests, Logistic Regression, and PageRank, running on a diverse set of semantic features. We ranked 1st in the top-N recommendation task, and 3rd in the tasks of rating prediction and diversity.
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Basile, P., Musto, C., de Gemmis, M., Lops, P., Narducci, F., Semeraro, G. (2014). Content-Based Recommender Systems + DBpedia Knowledge = Semantics-Aware Recommender Systems. In: Presutti, V., et al. Semantic Web Evaluation Challenge. SemWebEval 2014. Communications in Computer and Information Science, vol 475. Springer, Cham. https://doi.org/10.1007/978-3-319-12024-9_21
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DOI: https://doi.org/10.1007/978-3-319-12024-9_21
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