Linked Open Data to support Content-based Recommender Systems
Authors
Di Noia Tommaso, Mirizzi Roberto, Ostuni Vito Claudio, Romito Davide, Zanker MarkusAbstract
The World Wide Web is moving from a Web of hyper-linked
Documents to a Web of linked Data. Thanks to the Semantic Web spread and to the more recent Linked Open Data
(LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets. These datasets are con-
nected with each other to form the so called Linked Open
Data cloud. As of today, there are tons of RDF data available in the Web of Data, but only few applications really exploit their potential power. In this paper we show how these
data can successfully be used to develop a recommender system (RS) that relies exclusively on the information encoded
in the Web of Data. We implemented a content-based RS
that leverages the data available within Linked Open Data
datasets (in particular DBpedia, Freebase and LinkedMDB)
in order to recommend movies to the end users. We extensively evaluated the approach and validated the effectiveness
of the algorithms by experimentally measuring their accuracy with precision and recall metrics.
DOI
https://doi.org/10.1007/978-1-4939-7131-2_110165BibTex references
@InProceedings{DMORZ12,
author = "Di Noia, Tommaso and Mirizzi, Roberto and Ostuni, Vito Claudio and Romito, Davide and Zanker, Markus",
title = "Linked Open Data to support Content-based Recommender Systems",
booktitle = "8th International Conference on Semantic Systems (I-SEMANTICS 2012)",
series = "ICP",
year = "2012",
publisher = "ACM Press",
url = "http://sisinflab.poliba.it/Publications/2012/DMORZ12"
}