Linked open data to support content-based recommender systems

By Tommaso Di Noia, R. Mirizzi, Vito Claudio Ostuni, David Romito, Markus Zanker. 2012

312 citations recorded by the source corpus.

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 connected 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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

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Citations in the corpus

Knowledge-aware Autoencoders for Explainable Recommender Systems (2018, background): “Looking at the last mentioned method, it is worth noticing how Knowledge Graphs (KG) are recently being used in lots of applications; they freely offer a large amount of structured data which turned out to be very useful also in recommendation scenarios [4, 5, 12].”
Computing Recommendations via a Knowledge Graph-aware Autoencoder (2018, methodology): “By leveraging the knowledge encoded in DBpedia, it is possible to build an accurate content-based recommender system [9] also in mobile scenarios [33] or a multirelational graph for graph-based recommenders [11].”
Semantic Web in data mining and knowledge discovery: A comprehensive survey (2016, methodology): “, explicitly listing, for each property, the values which are common between themovies in the user profile and the suggested ones [194].”
Domain-specific Knowledge Extraction from the Web of Data (2018, methodology): “They play a major role in intelligent applications such as personalization [Kapanipathi et al. 2014], question answering [Welty et al. 2012], and recommendation systems [Ostuni et al. 2013] [Musto et al. 2014] [Di Noia et al. 2012] [Piao and Breslin 2016][Cheekula et al. 2015].”
A Review of Content-Based and Context-Based Recommendation Systems (2021, background): “Gigantic scientific categorizations for item orders are transitioning, e.g., the United Nations Standard Products and Services Classification (UNSPSC, for example, Amazon.com order scientific categorizations for books, DVDs, CDs, and clothing [40].”
Citation recommendation: approaches and datasets (2020, unknown): “In particular, the type of citation, given as the citation function or in the form of another classiﬁcation, such as whether the citation backs up a single concept or a claim, seems to be a notable aspect to be considered.”