Towards Semantics-Aware Recommender System: A LOD-Based Approach

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Asmaa Fridi 1,* Sidi Mohamed Benslimane 2

1. EEDIS Laboratory, Djillali Liabes University of Sidi Bel Abbes, Sidi Bel Abbes, 22000, Algeria

2. LabRI-SBA Laboratory, École Supérieure en Informatique, Sidi Bel Abbes, Algeria

* Corresponding author.


Received: 23 Oct. 2016 / Revised: 9 Nov. 2016 / Accepted: 16 Dec. 2016 / Published: 8 Feb. 2017

Index Terms

Recommender system, Collaborative filtering, Content-based filtering, Linked Open Data, Clustering


Recommender systems have contributed to the success of personalized websites as they can automatically and efficiently select items or services adapted to the user's interest from huge datasets. However, these systems suffer of issues related to small number of evaluations; cold start system and data sparsity. Several approaches have been explored to find solutions to related issues. The advent of the Linked Open Data (LOD) initiative has spawned a wide range of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper, we aim to demonstrate that adding semantic information from LOD enhance the effectiveness of traditional collaborative filtering. To evaluate the accuracy of the semantic approach, experiments on standard benchmark dataset was conducted. The obtained results indicate that the accuracy and quality of the recommendation are improved compared with existing approaches.

Cite This Paper

Asmaa Fridi, Sidi Mohamed Benslimane, "Towards Semantics-Aware Recommender System: A LOD-Based Approach", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.2, pp.55-61, 2017. DOI:10.5815/ijmecs.2017.02.07


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