IDEAS home Printed from https://ideas.repec.org/a/ids/ijdmmm/v6y2014i4p333-346.html
   My bibliography  Save this article

Using trajectories for collaborative filtering-based POI recommendation

Author

Listed:
  • Haosheng Huang
  • Georg Gartner

Abstract

Current mobile guides often suffer from the following problems: a long knowledge acquisition process of recommending relevant points of interest (POIs), the lack of social navigation support, and the challenge of making implicit user-generated content (e.g., trajectories) useful. Collaborative filtering (CF) is a promising solution for these problems. This article employs CF to mine GPS trajectories for providing Amazon-like POI recommendations. Three CF methods are designed: simple_CF, freq_CF (considering visit frequencies of POIs), and freq_seq_CF (considering both user's preferences and spatio-temporal behaviour). With these, services like "after visiting ..., people similar to you often went to ..." can be provided. The methods are evaluated with two GPS datasets. The results show that the CF methods can provide more accurate predictions than simple location-based methods. Also considering visit frequencies (popularity) of POIs and spatio-temporal motion behaviour (mainly the ways in which POIs are visited) in CF can improve the predictive performance.

Suggested Citation

  • Haosheng Huang & Georg Gartner, 2014. "Using trajectories for collaborative filtering-based POI recommendation," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 6(4), pages 333-346.
    • Handle: RePEc:ids:ijdmmm:v:6:y:2014:i:4:p:333-346
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=66762
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Xia, Dawen & Jiang, Shunying & Li, Yunsong & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing, 2023. "An ASM-CF model for anomalous trajectory detection with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    2. Zheng, Weimin & Liao, Zhixue & Qin, Jing, 2017. "Using a four-step heuristic algorithm to design personalized day tour route within a tourist attraction," Tourism Management, Elsevier, vol. 62(C), pages 335-349.
    3. David Massimo & Francesco Ricci, 2021. "Popularity, novelty and relevance in point of interest recommendation: an experimental analysis," Information Technology & Tourism, Springer, vol. 23(4), pages 473-508, December.

    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:ids:ijdmmm:v:6:y:2014:i:4:p:333-346. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=342 .

    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.