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Combining Linked Open Data Similarity and Relatedness for Cross OSN Recommendation

Author

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  • Mohamed Boubenia

    (University of Sciences and Technology Houari Boumediene, Algeria)

  • Abdelkader Belkhir

    (University of Sciences and Technology Houari Boumediene, Algeria)

  • Fayçal M'hamed Bouyakoub

    (University of Sciences and Technology Houari Boumediene, Algeria)

Abstract

The emergence of online social networks (OSNs) and linked open data (LOD) bring up opportunities to experiment on a new generation of cross-domain recommender systems in which the true benefit of LOD can be exploited, particularly to address the new user problems. In this article, the authors explore the feasibility of combining the two axes of comparison, similarity and relatedness, in LOD space, and introduce a new LOD-based similarity measure. The reason is to take benefit more from LOD to compare general resources, which can be useful in the context of cross-OSN recommendation. Experimental evaluation demonstrates the effectiveness of the proposed approach.

Suggested Citation

  • Mohamed Boubenia & Abdelkader Belkhir & Fayçal M'hamed Bouyakoub, 2020. "Combining Linked Open Data Similarity and Relatedness for Cross OSN Recommendation," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 16(2), pages 59-90, April.
    • Handle: RePEc:igg:jswis0:v:16:y:2020:i:2:p:59-90
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