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Power of Bosom Friends, POI Recommendation by Learning Preference of Close Friends and Similar Users

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Big Data Analytics and Knowledge Discovery (DaWaK 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9829))

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Abstract

With the emergence of social networks, mining interesting information from the social media datasets becomes a popular research direction. Previous researches on social networks, such as POI (point of interest) recommendation, usually ignore the social tie strength between users. If we can further consider the closeness between friends in the analysis, it is possible to improve the results. Therefore, in this paper, we focus on analyzing the social tie strength between users in the location-based social network. The proposed method analyzes the movement of users and the interaction between them by the spatial-temporal data. Furthermore, the social relationship structure is also taken into consideration for the calculation of the social tie strength. Finally, the location list for POI recommendation will be constructed accordingly. Experimental results show that the proposed method significantly outperforms the competitor on both precision and recall.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, ROC

    Mu-Yao Fang & Bi-Ru Dai

Authors
  1. Mu-Yao Fang
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  2. Bi-Ru Dai
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Correspondence to Bi-Ru Dai .

Editor information

Editors and Affiliations

  1. University of Science and Technology , Rolla, Missouri, USA

    Sanjay Madria

  2. Osaka University , Osaka, Japan

    Takahiro Hara

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Fang, MY., Dai, BR. (2016). Power of Bosom Friends, POI Recommendation by Learning Preference of Close Friends and Similar Users. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2016. Lecture Notes in Computer Science(), vol 9829. Springer, Cham. https://doi.org/10.1007/978-3-319-43946-4_12

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  • DOI: https://doi.org/10.1007/978-3-319-43946-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43945-7

  • Online ISBN: 978-3-319-43946-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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