IDEAS home Printed from https://ideas.repec.org/a/igg/jdwm00/v6y2010i2p59-78.html
   My bibliography  Save this article

User-Centric Similarity and Proximity Measures for Spatial Personalization

Author

Listed:
  • Yanwu Yang

    (Chinese Academy of Sciences, China)

  • Christophe Claramunt

    (Naval Academy Research Institute, France)

  • Marie-Aude Aufaure

    (Ecole Centrale Paris, France)

  • Wensheng Zhang

    (Chinese Academy of Sciences, China)

Abstract

Spatial personalization can be defined as a novel way to fulfill user information needs when accessing spatial information services either on the web or in mobile environments. The research presented in this paper introduces a conceptual approach that models the spatial information offered to a given user into a user-centered conceptual map, and spatial proximity and similarity measures that considers her/his location, interests and preferences. This approach is based on the concepts of similarity in the semantic domain, and proximity in the spatial domain, but taking into account user’s personal information. Accordingly, these spatial proximity and similarity measures could directly support derivation of personalization services and refinement of the way spatial information is accessible to the user in spatially related applications. These modeling approaches are illustrated by some experimental case studies.

Suggested Citation

  • Yanwu Yang & Christophe Claramunt & Marie-Aude Aufaure & Wensheng Zhang, 2010. "User-Centric Similarity and Proximity Measures for Spatial Personalization," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 6(2), pages 59-78, April.
    • Handle: RePEc:igg:jdwm00:v:6:y:2010:i:2:p:59-78
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jdwm.2010040104
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sultan Alamri & David Taniar & Maytham Safar, 2013. "Indexing moving objects for directions and velocities queries," Information Systems Frontiers, Springer, vol. 15(2), pages 235-248, April.
    2. Daniel Zeng & Yong Liu & Ping Yan & Yanwu Yang, 2021. "Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1608-1623, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jdwm00:v:6:y:2010:i:2:p:59-78. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.