ReDyAl: A Dynamic Recommendation Algorithm based on Linked Data

Iacopo Vagliano 1 Cristhian Figueroa 1 Oscar Rodríguez Rocha 2, 3 Marco Torchiano 1 Catherine Faron Zucker 2, 3 Maurizio Morisio 1
1 DAUIN - Dipartimento di Automatica e Informatica [Torino]
2 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis
3 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : The Web of Data is an interconnected global dataspace in which discovering resources related to a given resource and recommend relevant ones is still an open research area. This work describes a new recommendation algorithm based on structured data published on the Web (Linked Data). The algorithm exploits existing relationships between resources by dynamically analyzing both the categories to which they belong to and their explicit references to other resources. A user study conducted to evaluate the algorithm showed that our algorithm provides more novel recommendations than other state-of-the-art algorithms and keeps a satisfying prediction accuracy. The algorithm has been applied in a mobile application to recommend movies by relying on DBpedia (the Linked Data version of Wikipedia), although it could be applied to other datasets on the Web of Data.
Keywords : Linked Data Recommender System Semantic Web DBpedia
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Conference papers
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https://hal.inria.fr/hal-01379996
Contributor : Oscar Rodríguez Rocha <>
Submitted on : Wednesday, October 12, 2016 - 12:13:31 PM
Last modification on : Monday, November 5, 2018 - 3:52:09 PM
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Iacopo Vagliano, Cristhian Figueroa, Oscar Rodríguez Rocha, Marco Torchiano, Catherine Faron Zucker, et al.. ReDyAl: A Dynamic Recommendation Algorithm based on Linked Data. RecSys 2016 - 3rd Workshop on New Trends in Content-Based Recommender Systems co-located with ACM Conference on Recommender Systems, Sep 2016, Boston, United States. ⟨hal-01379996⟩

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