IDEAS home Printed from https://ideas.repec.org/a/igg/jwsr00/v14y2017i2p45-66.html
   My bibliography  Save this article

A Location-Context Awareness Mobile Services Collaborative Recommendation Algorithm Based on User Behavior Prediction

Author

Listed:
  • Mingjun Xin

    (School of Computer Engineering and Science, Shanghai University, Shanghai, China)

  • Yanhui Zhang

    (School of Computer Engineering and Science, Shanghai University, Shanghai, China)

  • Shunxiang Li

    (School of Computer Engineering and Science, Shanghai University, Shanghai, China)

  • Liyuan Zhou

    (School of Computer Engineering and Science, Shanghai University, Shanghai, China)

  • Weimin Li

    (School of Computer Engineering and Science, Shanghai University, Shanghai, China)

Abstract

Nowadays, location based services (LBS) has become one of the most popular applications with the rapid development of mobile Internet technology. More and more research is focused on discovering the required services among massive information according to the personalized behavior. In this paper, a collaborative filtering (CF) recommendation algorithm is presented based on the Location-aware Hidden Markov Model (LHMM). This approach includes three main stages. First, it clusters users by making a pattern similarity calculation of their historical check-in data. Then, it establishes the location-aware transfer matrix so as to get the next most similar service. Furthermore, it integrates the generated LHMM, user's score and interest migration into the traditional CF algorithm so as to generate a final recommendation list. The LHMM-based CF algorithm mixes the geographic factors and personalized behavior and experimental results show that it outperforms the state-of-the-art algorithms on both precision and recall.

Suggested Citation

  • Mingjun Xin & Yanhui Zhang & Shunxiang Li & Liyuan Zhou & Weimin Li, 2017. "A Location-Context Awareness Mobile Services Collaborative Recommendation Algorithm Based on User Behavior Prediction," International Journal of Web Services Research (IJWSR), IGI Global, vol. 14(2), pages 45-66, April.
    • Handle: RePEc:igg:jwsr00:v:14:y:2017:i:2:p:45-66
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJWSR.2017040103
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Xiaolan Xie & Xun Zhang & Jingying Fu & Dong Jiang & Chongchong Yu & Min Jin, 2018. "Location Recommendation of Digital Signage Based on Multi-Source Information Fusion," Sustainability, MDPI, vol. 10(7), pages 1-21, July.

    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:jwsr00:v:14:y:2017:i:2:p:45-66. 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.