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Hybrid Recommending Exploiting Multiple DBPedia Language Editions

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Semantic Web Evaluation Challenge (SemWebEval 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 475))

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Abstract

In this paper we describe approach of our SemWex1 group to the ESWC 2014 RecSys Challenge. Our method is based on using an adaptation of Content Boosted Matrix factorization [1], where objects are defined through their content-based features. Features were comprised of both direct DBPedia RDF triples and derived semantic information (with some WIE and NLP features). Total of seven DBPedia language editions were used to form the dataset. In the paper we will further describe our methods for semantic information creation, data filtration, algorithm details and settings as well as decisions made during the challenge and dead ends we explored.

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References

  1. Forbes, P., Zhu, M.: Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation. In: RecSys 2011, pp. 261–264. ACM (2011)

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  4. Peska, L., Vojtas, P.: Using LOD to improve recommending on e-commerce. In: SerSy’13 (2013). http://www.ksi.mff.cuni.cz/~peska/sersy13.pdf

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Acknowledgments

This work was supported by grants SVV-2014-260100, P46 and GAUK-126313.

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Authors and Affiliations

  1. Faculty of Mathematics and Physics, Charles University in Prague, Malostranske namesti 25, Prague, Czech Republic

    Ladislav Peska & Peter Vojtas

Authors
  1. Ladislav Peska
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  2. Peter Vojtas
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Corresponding author

Correspondence to Ladislav Peska .

Editor information

Editors and Affiliations

  1. Semantic Technology Laboratory, ISTC-CNR, Rome, Italy

    Valentina Presutti

  2. Université Paris-Sorbonne,, Paris, France

    Milan Stankovic

  3. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

  4. Universidad Autónoma de Madrid, Madrid, Spain

    Iván Cantador

  5. University of Bologna, Bologna, Italy

    Angelo Di Iorio

  6. Polytechnic University of Bari, Bari, Italy

    Tommaso Di Noia

  7. University of Birmingham, Birmingham, United Kingdom

    Christoph Lange

  8. ISTC-CNR, Semantic Technology Laboratory, Rome, Italy

    Diego Reforgiato Recupero

  9. Elsevier B.V., Amsterdam, The Netherlands

    Anna Tordai

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© 2014 Springer International Publishing Switzerland

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Peska, L., Vojtas, P. (2014). Hybrid Recommending Exploiting Multiple DBPedia Language Editions. In: Presutti, V., et al. Semantic Web Evaluation Challenge. SemWebEval 2014. Communications in Computer and Information Science, vol 475. Springer, Cham. https://doi.org/10.1007/978-3-319-12024-9_18

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  • DOI: https://doi.org/10.1007/978-3-319-12024-9_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12023-2

  • Online ISBN: 978-3-319-12024-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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