LOOKER: a mobile, personalized recommender system in the tourism domain based on social media user-generated content

Abstract

In a ubiquitous computing scenario, characterized by pervasive technologies, tourists can get assistance from mobile technologies in planning their trips. In a context where more and more people own smartphones, tourists expect to get personalized suggestions just in time whenever and wherever they need. To be effective, mobile applications for travel recommendation should consider both the variability of the user’s interests and an effective way to express them while interacting with the environment. This paper presents LOOKER, a mobile recommender system for tourism and travel-related services that considers the above-described issues. It is an adaptable application developed for the Android platform, which takes into account basic contextual information such as location and time, and implements a content-based filtering (CBF) strategy to make personalized suggestions based on the user’s tourism-related user-generated content (UGC) s/he diffuses on social media. Specifically, the CBF strategy implemented in LOOKER is based on a multi-layer user profile, where the layers representing distinct travel-related service categories (e.g., restaurants, hotels, points of interest) are modeled via language models that are defined on the basis of the captured UGC. This allows inferring the interests and the opinions of travelers about the available items. To evaluate the usefulness and the usability of the LOOKER mobile application, user studies have been conducted. The positive outcomes that have been obtained illustrate the potentials of LOOKER.

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Notes

  1. 1.

    https://developers.facebook.com/

  2. 2.

    https://dev.twitter.com/overview/api/

  3. 3.

    https://developer-tripadvisor.com/content-api/

  4. 4.

    http://www.expedia.com/

  5. 5.

    http://www.trippy.com/

  6. 6.

    Recent empirical analyses by Panniello et al. [51] demonstrated that no CARS paradigm in particular dominates the others across all domains.

  7. 7.

    Hence, diminishing the number of similarity comparisons that should be made between the user profile and the possible items to be recommended.

  8. 8.

    https://developers.google.com/places/

  9. 9.

    This strategy could be coupled in future works with negative filtering, i.e., the process of filtering out those TR services that the user dislikes.

  10. 10.

    https://github.com/Ruthwik/Sentiment-Analysis/

  11. 11.

    http://www.tunav.com/

  12. 12.

    http://www.frandroid.com/tag/android-6-0-marshmallow/

  13. 13.

    Source IDC, 2018: https://www.idc.com/promo/smartphone-market-share/os/

  14. 14.

    The on-boarding process, in mobile application, refers to the mechanism through which new users are informed and educated about the application. A strong on-boarding process is essential for the success of the mobile app [46].

  15. 15.

    http://www.isgs.rnu.tn/

  16. 16.

    http://www.esct.rnu.tn/site/

  17. 17.

    http://www.ihec.rnu.tn/

  18. 18.

    This strategy aims at tackling a major issue in evaluating CARS, i.e., the lack of context-enriched datasets [30].

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Correspondence to Marco Viviani.

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Missaoui, S., Kassem, F., Viviani, M. et al. LOOKER: a mobile, personalized recommender system in the tourism domain based on social media user-generated content. Pers Ubiquit Comput 23, 181–197 (2019). https://doi.org/10.1007/s00779-018-01194-w

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Keywords

  • Mobile recommender systems
  • Content-based filtering
  • Personalization
  • Language models
  • User-generated content
  • Social media