Point-of-interest lists and their potential in recommendation systems
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DOI: 10.1007/s40558-021-00195-5
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- 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.
- 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.
- 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.
- 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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Cited by:
- 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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Keywords
Points of interest; Foursquare lists; Recommendation systems; Bipartite networks;All these keywords.
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