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A Business Zone Recommender System Based on Facebook and Urban Planning Data

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Advances in Information Retrieval (ECIR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9626))

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Abstract

We present ZoneRec—a zone recommendation system for physical businesses in an urban city, which uses both public business data from Facebook and urban planning data. The system consists of machine learning algorithms that take in a business’ metadata and outputs a list of recommended zones to establish the business in. We evaluate our system using data of food businesses in Singapore and assess the contribution of different feature groups to the recommendation quality.

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Acknowledgments

This research is supported by the Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiative and administered by the IDM Programme Office, Media Development Authority (MDA).

Author information

Authors and Affiliations

  1. Living Analytics Research Centre, Singapore Management University, Singapore, Singapore

    Jovian Lin, Richard J. Oentaryo, Ee-Peng Lim, Casey Vu, Adrian Vu, Agus T. Kwee & Philips K. Prasetyo

Authors
  1. Jovian Lin
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  2. Richard J. Oentaryo
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  3. Ee-Peng Lim
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  4. Casey Vu
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  5. Adrian Vu
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  6. Agus T. Kwee
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  7. Philips K. Prasetyo
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Corresponding author

Correspondence to Jovian Lin .

Editor information

Editors and Affiliations

  1. Department of Information Engineering, University of Padua, Padova, Italy

    Nicola Ferro

  2. Faculty of Informatics, University of Lugano (USI), Lugano, Switzerland

    Fabio Crestani

  3. Department of Computer Science, Katholieke Universiteit Leuven, Heverlee, Belgium

    Marie-Francine Moens

  4. Systèmes d’informations, Big Data et Recherche d’Information, Institut de Recherche en Informatique de Toulouse IRIT/équipe SIG, Toulouse Cedex 04, France

    Josiane Mothe

  5. Yahoo! Labs London, London, UK

    Fabrizio Silvestri

  6. Department of Information Engineering, University of Padua, Padova, Italy

    Giorgio Maria Di Nunzio

  7. TU Delft - EWI/ST/WIS, Delft, The Netherlands

    Claudia Hauff

  8. Department of Information Engineering, University of Padua, Padova, Italy

    Gianmaria Silvello

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© 2016 Springer International Publishing Switzerland

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Lin, J. et al. (2016). A Business Zone Recommender System Based on Facebook and Urban Planning Data. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_47

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  • DOI: https://doi.org/10.1007/978-3-319-30671-1_47

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30670-4

  • Online ISBN: 978-3-319-30671-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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