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Flexible On-the-Fly Recommendations from Linked Open Data Repositories

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Business Information Systems (BIS 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 255))

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Abstract

Recommender systems help consumers to find products online. But because many content-based systems work with insufficient data, recent research has focused on enhancing item feature information with data from the Linked Open Data cloud. Linked Data recommender systems are usually bound to a predefined set of item features and offer limited opportunities to tune the recommendation model to individual needs. The paper addresses this research gap by introducing the prototype SKOS Recommender (SKOSRec), which produces scalable on-the-fly recommendations through SPARQL-like queries from Linked Data repositories. The SKOSRec query language enables users to obtain constraint-based, aggregation-based and cross-domain recommendations, such that results can be adapted to specific business or customer requirements.

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Notes

  1. 1.

    http://www.amazon.de.

  2. 2.

    http://www.netflix.com.

  3. 3.

    http://wiki.dbpedia.org/.

  4. 4.

    https://jena.apache.org/.

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Author information

Authors and Affiliations

  1. Chair of Business Information Systems, Friedrich-Schiller-University, Jena, Germany

    Lisa Wenige & Johannes Ruhland

Authors
  1. Lisa Wenige
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  2. Johannes Ruhland
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Corresponding author

Correspondence to Lisa Wenige .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Poznan Univ of Economics and Business, Poznan, Poland

    Witold Abramowicz

  2. Institut für Wirtschaftsinformatik, Universität Leipzig, Leipzig, Sachsen, Germany

    Rainer Alt

  3. Information System Institute, University of Leipzig, Leipzig, Germany

    Bogdan Franczyk

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Wenige, L., Ruhland, J. (2016). Flexible On-the-Fly Recommendations from Linked Open Data Repositories. In: Abramowicz, W., Alt, R., Franczyk, B. (eds) Business Information Systems. BIS 2016. Lecture Notes in Business Information Processing, vol 255. Springer, Cham. https://doi.org/10.1007/978-3-319-39426-8_4

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