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TSRS: Trip Service Recommended System Based on Summarized Co-location Patterns

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Web and Big Data (APWeb-WAIM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10987))

Abstract

Co-location patterns, whose instances are frequently located together, are particularly valuable for many applications. With co-location patterns, the location-based service recommendation can be made to give guidance to the user’s trip. However, the number of co-location patterns is typically huge, thus it is restricted for practical applications. Based on summarized co-location patterns, we design a trip service recommended system, named TSRS. In TSRS, a large number of co-location patterns are compressed into a small quantity of summarized co-location patterns and their instances are stored into the retrieval tree for fast querying. Furthermore, TSRS provides the service point recommendation according to summarized co-location patterns, and route planning is given to help the user get to service points conveniently.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China (61472346, 61662086, 61762090), the Natural Science Foundation of Yunnan Province (2015FB114, 2016FA026), the Project of Innovative Research Team of Yunnan Province, and the Project of Yunnan University Graduate Student Scientific Research (YDY17110).

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Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, 650091, China

    Peizhong Yang, Tao Zhang & Lizhen Wang

Authors
  1. Peizhong Yang
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  2. Tao Zhang
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  3. Lizhen Wang
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Corresponding author

Correspondence to Lizhen Wang .

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Editors and Affiliations

  1. South China University of Technology, Guangzhou, China

    Yi Cai

  2. Nagoya University, Nagoya, Japan

    Yoshiharu Ishikawa

  3. Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

    Jianliang Xu

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Yang, P., Zhang, T., Wang, L. (2018). TSRS: Trip Service Recommended System Based on Summarized Co-location Patterns. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_37

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  • DOI: https://doi.org/10.1007/978-3-319-96890-2_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96889-6

  • Online ISBN: 978-3-319-96890-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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