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

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

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), and Maurizio Morisio (Politecnico di Torino, Torino, Italy)
Copyright: © 2017 |Volume: 13 |Issue: 4 |Pages: 21
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781522511601|DOI: 10.4018/IJSWIS.2017100107
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MLA

Figueroa, Cristhian, et al. "Allied: A Framework for Executing Linked Data-Based Recommendation Algorithms." IJSWIS vol.13, no.4 2017: pp.134-154. https://doi.org/10.4018/IJSWIS.2017100107

APA

Figueroa, C., Vagliano, I., Rocha, O. R., Torchiano, M., Zucker, C. F., Corrales, J. C., & Morisio, M. (2017). Allied: A Framework for Executing Linked Data-Based Recommendation Algorithms. International Journal on Semantic Web and Information Systems (IJSWIS), 13(4), 134-154. https://doi.org/10.4018/IJSWIS.2017100107

Chicago

Figueroa, Cristhian, et al. "Allied: A Framework for Executing Linked Data-Based Recommendation Algorithms," International Journal on Semantic Web and Information Systems (IJSWIS) 13, no.4: 134-154. https://doi.org/10.4018/IJSWIS.2017100107

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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.

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