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Journal Articles International Journal on Semantic Web and Information Systems Year : 2017

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

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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Dates and versions

hal-01619781 , version 1 (10-11-2017)

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Cristhian Figueroa, Iacopo Vagliano, Oscar Rodríguez Rocha, Marco Torchiano, Catherine Faron Zucker, et al.. Allied: A Framework for Executing Linked Data- Based Recommendation Algorithms. International Journal on Semantic Web and Information Systems, 2017, 13 (4), pp.134 - 154. ⟨10.4018/IJSWIS.2017100107⟩. ⟨hal-01619781⟩
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