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Point-of-interest lists and their potential in recommendation systems

Author

Listed:
  • Giorgos Stamatelatos

    (Institute for Language and Speech Processing, Athena Research Center
    Democritus University of Thrace)

  • George Drosatos

    (Institute for Language and Speech Processing, Athena Research Center)

  • Sotirios Gyftopoulos

    (Institute for Language and Speech Processing, Athena Research Center
    Democritus University of Thrace)

  • Helen Briola

    (Institute for Language and Speech Processing, Athena Research Center
    Democritus University of Thrace)

  • Pavlos S. Efraimidis

    (Institute for Language and Speech Processing, Athena Research Center
    Democritus University of Thrace)

Abstract

Location based social networks, such as Foursquare and Yelp, have inspired the development of novel recommendation systems due to the massive volume and multiple types of data that their users generate on a daily basis. More recently, research studies have been focusing on utilizing structural data from these networks that relate the various entities, typically users and locations. In this work, we investigate the information contained in unique structural data of social networks, namely the lists or collections of items, and assess their potential in recommendation systems. Our hypothesis is that the information encoded in the lists can be utilized to estimate the similarities amongst POIs and, hence, these similarities can drive a personalized recommendation system or enhance the performance of an existing one. This is based on the fact that POI lists are user generated content and can be considered as collections of related POIs. Our method attempts to extract these relations and express the notion of similarity using graph theoretic, set theoretic and statistical measures. Our approach is applied on a Foursquare dataset of two popular destinations in northern Greece and is evaluated both via an offline experiment and against the opinions of local populace that we obtain via a user study. The results confirm the existence of rich similarity information within the lists and the effectiveness of our approach as a recommendation system.

Suggested Citation

  • Giorgos Stamatelatos & George Drosatos & Sotirios Gyftopoulos & Helen Briola & Pavlos S. Efraimidis, 2021. "Point-of-interest lists and their potential in recommendation systems," Information Technology & Tourism, Springer, vol. 23(2), pages 209-239, June.
    • Handle: RePEc:spr:infott:v:23:y:2021:i:2:d:10.1007_s40558-021-00195-5
    DOI: 10.1007/s40558-021-00195-5
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    References listed on IDEAS

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    1. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    2. Egbert Van der Zee & Dario Bertocchi, 2018. "Finding patterns in urban tourist behaviour: a social network analysis approach based on TripAdvisor reviews," Information Technology & Tourism, Springer, vol. 20(1), pages 153-180, December.
    3. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    4. Tatiana David-Negre & Arminda Almedida-Santana & Juan M. Hernández & Sergio Moreno-Gil, 2018. "Understanding European tourists’ use of e-tourism platforms. Analysis of networks," Information Technology & Tourism, Springer, vol. 20(1), pages 131-152, December.
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    1. Almudena Nolasco-Cirugeda & Clara García-Mayor & Cristina Lupu & Alvaro Bernabeu-Bautista, 2022. "Scoping out urban areas of tourist interest though geolocated social media data: Bucharest as a case study," Information Technology & Tourism, Springer, vol. 24(3), pages 361-387, September.

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