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A Spatial User Similarity Measure for Geographic Recommender Systems

  • Conference paper
GeoSpatial Semantics (GeoS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5892))

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Abstract

Recommender systems solve an information filtering task. They suggest data objects that seem likely to be relevant to the user based upon previous choices that this user has made. A geographic recommender system recommends items from a library of georeferenced objects such as photographs of touristic sites. A widely-used approach to recommending consists in suggesting the most popular items within the user community. However, these approaches are not able to handle individual differences between users. We ask how to identify less popular geographic objects that are nevertheless of interest to a specific user. Our approach is based on user-based collaborative filtering in conjunction with an prototypical model of geographic places (heatmaps). We discuss four different measures of similarity between users that take into account the spatial semantic derived from the spatial behavior of a user community. We illustrate the method with a real-world use case: recommendations of georeferenced photographs from the public website Panoramio. The evaluation shows that our approach achieves a better recall and precision for the first ten items than recommendations based on the most popular geographic items.

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Author information

Authors and Affiliations

  1. Laboratory for Semantic Information Technology, University of Bamberg, Feldkirchenstrasse 21, 96052, Bamberg, Germany

    Christian Matyas & Christoph Schlieder

Authors
  1. Christian Matyas
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  2. Christoph Schlieder
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Editor information

Editors and Affiliations

  1. Department of Geography, The Pennsylvania State University, University Park, 16802, PA, USA

    Krzysztof Janowicz

  2. Department of Geography, University of California, Santa Barbara, 93106, CA, USA

    Martin Raubal

  3. Centro de Investigacion en Computacion, National Polytechnic Institute, 07738, Mexico City, Mexico

    Sergei Levashkin

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Matyas, C., Schlieder, C. (2009). A Spatial User Similarity Measure for Geographic Recommender Systems. In: Janowicz, K., Raubal, M., Levashkin, S. (eds) GeoSpatial Semantics. GeoS 2009. Lecture Notes in Computer Science, vol 5892. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10436-7_8

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  • DOI: https://doi.org/10.1007/978-3-642-10436-7_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10435-0

  • Online ISBN: 978-3-642-10436-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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