A roadmap for privacy preserving tourist recommendation system

Alan J. Wecker, Noa Tuval, Alain Hertz, Muhammad Mahamid, Tsvi Kuflik

Research output: Contribution to journalConference articlepeer-review

Abstract

Users’ privacy is one of the main concerns of users who use recommender systems in general and tourist recommender systems in particular, due to the fact that they must share personal information (like preferences and location) with the system in exchange for recommendations. The personal information collected by the system is used for creating user models used for personalization of recommendations, but may be used and / or shared or sold to 3rd parties. Still, when considering content-based recommender systems, the situation may be different if the user’s model is built, maintained and stored locally on the user’s device/personal cloud. The paper presents a simple yet effective privacy preserving content-based recommender system architecture that uses a hypercube-based model for representing user preferences.

Original languageEnglish
Pages (from-to)68-73
Number of pages6
JournalCEUR Workshop Proceedings
Volume3886
StatePublished - 2024
Event2024 Workshop on Recommenders in Tourism, RecTour 2024 - Bari, Italy
Duration: 18 Sep 2024 → …

Bibliographical note

Publisher Copyright:
© 2024 Copyright for this paper by its authors.

Keywords

  • content-based recommender system
  • Hypercube-based recommender
  • privacy preserving
  • recommender system
  • system

ASJC Scopus subject areas

  • General Computer Science

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