Allied: A Framework for Executing Linked Data-Based Recommendation Algorithms

Cristhian Figueroa, Iacopo Vagliano, Oscar Rodriguez Rocha, Marco Torchiano, Catherine Faron Zucker, Juan Carlos Corrales and Maurizio Morisio
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Cristhian Figueroa: Politecnico di Torino, Torino, Italy & Universidad del Cauca, Popayan, Colombia
Iacopo Vagliano: Politecnico di Torino, Torino, Italy
Oscar Rodriguez Rocha: INRIA Sophia Antipolis MNRIA Sophia, Sophia Antipolis, France
Marco Torchiano: Politecnico di Torino, Torino, Italy
Catherine Faron Zucker: Universitf Nice Sophia Antipolis, Sophia Antipolis, France
Juan Carlos Corrales: Universidad del Cauca, Popayan, Colombia
Maurizio Morisio: Politecnico di Torino, Torino, Italy

International Journal on Semantic Web and Information Systems (IJSWIS), 2017, vol. 13, issue 4, 134-154

Abstract: The increase in the amount of structured data published on the Web using the principles of Linked Data means that now it is more likely to find resources on the Web of Data that represent real life concepts. Discovering and recommending resources on the Web of Data related to a given resource is still an open research area. This work presents a framework to deploy and execute Linked Data based recommendation algorithms to measure their accuracy and performance in different contexts. Moreover, application developers can use this framework as the main component for recommendation in various domains. Finally, this paper describes a new recommendation algorithm that adapts its behavior dynamically based on the features of the Linked Data dataset used. The results of a user study show that the algorithm proposed in this paper has better accuracy and novelty than other state-of-the-art algorithms for Linked Data.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jswis0:v:13:y:2017:i:4:p:134-154

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