IDEAS home Printed from https://ideas.repec.org/a/spr/infott/v23y2021i2d10.1007_s40558-021-00195-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40558-021-00195-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40558-021-00195-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.
    2. Kadri Sylejmani & Vigan Abdurrahmani & Arben Ahmeti & Egzon Gashi, 2024. "Solving the tourist trip planning problem with attraction patterns using meta-heuristic techniques," Information Technology & Tourism, Springer, vol. 26(4), pages 633-678, December.
    3. Yating Zhang & Hongbo Tan & Qi Jiao & Zhihao Lin & Zesen Fan & Dengming Xu & Zheng Xiang & Rob Law & Tianxiang Zheng, 2024. "A Predictive Model Based on TripAdvisor Textual Reviews: Early Destination Recommendations for Travel Planning," SAGE Open, , vol. 14(2), pages 21582440241, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rafiee, Samira & Salavati, Chiman & Abdollahpouri, Alireza, 2020. "CNDP: Link prediction based on common neighbors degree penalization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    2. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    3. Chunjiang Liu & Yikun Han & Haiyun Xu & Shihan Yang & Kaidi Wang & Yongye Su, 2024. "A Community Detection and Graph-Neural-Network-Based Link Prediction Approach for Scientific Literature," Mathematics, MDPI, vol. 12(3), pages 1-20, January.
    4. Kai Yang & Yuan Liu & Zijuan Zhao & Xingxing Zhou & Peijin Ding, 2023. "Graph attention network via node similarity for link prediction," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(3), pages 1-10, March.
    5. Mungo, Luca & Lafond, François & Astudillo-Estévez, Pablo & Farmer, J. Doyne, 2023. "Reconstructing production networks using machine learning," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    6. Yan Qi & Xin Zhang & Zhengyin Hu & Bin Xiang & Ran Zhang & Shu Fang, 2022. "Choosing the right collaboration partner for innovation: a framework based on topic analysis and link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5519-5550, September.
    7. Yu, Jiating & Wu, Ling-Yun, 2022. "Multiple Order Local Information model for link prediction in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    8. Bütün, Ertan & Kaya, Mehmet, 2019. "A pattern based supervised link prediction in directed complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1136-1145.
    9. Joon Hyung Cho & Jungpyo Lee & So Young Sohn, 2021. "Predicting future technological convergence patterns based on machine learning using link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5413-5429, July.
    10. Douglas Castilho & Tharsis T. P. Souza & Soong Moon Kang & Jo~ao Gama & Andr'e C. P. L. F. de Carvalho, 2021. "Forecasting Financial Market Structure from Network Features using Machine Learning," Papers 2110.11751, arXiv.org.
    11. Zhen Liu & Jia-Lin He & Komal Kapoor & Jaideep Srivastava, 2013. "Correlations between Community Structure and Link Formation in Complex Networks," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-10, September.
    12. Najari, Shaghayegh & Salehi, Mostafa & Ranjbar, Vahid & Jalili, Mahdi, 2019. "Link prediction in multiplex networks based on interlayer similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    13. Changping Zhao & Xinli Qi & Jin Wang & Fengyang Du & Xiaolan Shi, 2022. "Predicting Possible New Links to Future Global Plastic Waste Trade Networks," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
    14. Zhang, Ting & Zhang, Kun & Li, Xun & Lv, Laishui & Sun, Qi, 2021. "Semi-supervised link prediction based on non-negative matrix factorization for temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    15. Zhou, Tao, 2023. "Discriminating abilities of threshold-free evaluation metrics in link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    16. Xie He & Amir Ghasemian & Eun Lee & Aaron Clauset & Peter J. Mucha, 2024. "Sequential stacking link prediction algorithms for temporal networks," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    17. Chunning Wang & Fengqin Tang & Xuejing Zhao, 2023. "LPGRI: A Global Relevance-Based Link Prediction Approach for Multiplex Networks," Mathematics, MDPI, vol. 11(14), pages 1-15, July.
    18. Wahid-Ul-Ashraf, Akanda & Budka, Marcin & Musial, Katarzyna, 2019. "How to predict social relationships — Physics-inspired approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1110-1129.
    19. Chen, Guangfu & Xu, Chen & Wang, Jingyi & Feng, Jianwen & Feng, Jiqiang, 2020. "Robust non-negative matrix factorization for link prediction in complex networks using manifold regularization and sparse learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    20. Tofighy, Sajjad & Charkari, Nasrollah Moghadam & Ghaderi, Foad, 2022. "Link prediction in multiplex networks using intralayer probabilistic distance and interlayer co-evolving factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infott:v:23:y:2021:i:2:d:10.1007_s40558-021-00195-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.