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International Conference on Knowledge Science, Engineering and Management

KSEM 2015: Knowledge Science, Engineering and Management pp 721–731Cite as

GPS-Based Location Recommendation Using a Belief Network Model

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9403))

Abstract

With the increasing popularity of location-based services, location recommendation is one of important applications. In this paper, according to a user’s preference, we recommend locations to a user by extending an information retrieval model, namely, belief network model. We regard the user’s preference as a query, a location as a document, categories as index terms. And then, we use the belief network model to recommend locations to a user by adding expert information. Experimental results on a real world data set show that recommendation effectiveness can be improved.

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Author information

Authors and Affiliations

  1. International WIC Institute, Beijing University of Technology, Beijing, 100024, China

    Kunlei Zhu, Jiajin Huang & Ning Zhong

  2. Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, 371-0816, Japan

    Ning Zhong

Authors
  1. Kunlei Zhu
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  2. Jiajin Huang
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  3. Ning Zhong
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Corresponding author

Correspondence to Kunlei Zhu .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Songmao Zhang

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Martin Wirsing

  3. Southwest University, Chongqing, China

    Zili Zhang

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© 2015 Springer International Publishing Switzerland

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Zhu, K., Huang, J., Zhong, N. (2015). GPS-Based Location Recommendation Using a Belief Network Model. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_66

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  • DOI: https://doi.org/10.1007/978-3-319-25159-2_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25158-5

  • Online ISBN: 978-3-319-25159-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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