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Augmenting LOD-Based Recommender Systems Using Graph Centrality Measures

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Web Engineering (ICWE 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11496))

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Abstract

In this paper we investigate the incorporation of graph-based features into LOD path-based recommender systems, an approach that so far has received little attention. More specifically, we propose two normalisation procedures that adjust user-item path counts by the degree centrality of the nodes connecting them. Evaluation on the MovieLens 1M dataset shows that the linear normalisation approach yields a significant increase in recommendation accuracy as compared to the default case, especially in settings where the most popular movies are omitted. These results serve as a fruitful base for further incorporation of graph measures into recommender systems, and might help in establishing the recommendation diversity that has recently gained much attention.

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Author information

Authors and Affiliations

  1. Erasmus University Rotterdam, PO Box 1738, 3000 DR, Rotterdam, The Netherlands

    Bart van Rossum & Flavius Frasincar

Authors
  1. Bart van Rossum
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  2. Flavius Frasincar
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Correspondence to Flavius Frasincar .

Editor information

Editors and Affiliations

  1. Novosibirsk State Technical University, Novosibirsk, Russia

    Maxim Bakaev

  2. Erasmus University Rotterdam, Rotterdam, The Netherlands

    Flavius Frasincar

  3. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    In-Young Ko

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van Rossum, B., Frasincar, F. (2019). Augmenting LOD-Based Recommender Systems Using Graph Centrality Measures. In: Bakaev, M., Frasincar, F., Ko, IY. (eds) Web Engineering. ICWE 2019. Lecture Notes in Computer Science(), vol 11496. Springer, Cham. https://doi.org/10.1007/978-3-030-19274-7_2

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  • DOI: https://doi.org/10.1007/978-3-030-19274-7_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19273-0

  • Online ISBN: 978-3-030-19274-7

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