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Toward a Smart Tourism Recommender System: Applied to Tangier City

  • Zineb Aarab
  • Asmae Elghazi
  • Rajaa Saidi
  • Moulay Driss Rahmani
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 37)

Abstract

Context-awareness has captivated a lot of attention, especially in the field of mobile and pervasive computing. Indeed, an important demand for real-world location data within the virtual world is growing. Yet, user’s context is more than its location. Most information systems do not take into account the diversity of users’ need and preferences because of its complexity to manage. Context-Aware Systems (CAS) are self-adaptive. Thus, automating the development of such systems and the interpretation of user’s preferences is a real challenge. One way to achieve this challenge is by discovering knowledge for prediction regarding actions of users with the systems (their history and so on). In this article, we explore how context information can be exploit to create intelligent and adaptive recommender systems. It provides an overview of the multiform notion of context with a discussion of several context oriented approaches and systems, and illustrates the usage of such approaches in several application areas. In this paper, we present our vision of the notion of context through a context metamodel. We also present an effective tourist recommendation system that respect personal preferences and capture usage, personal, social and environmental contextual parameters. A case study applied has been conducted to Tangier, a Moroccan city where a new intelligent international city will be constructed for the next decade. The article concludes by discussing the challenges and future research directions for context-aware recommender systems.

Keywords

Context-Aware system Mobile recommender system Mobile tourism Location-awareness Adaptive system Smart tourism Smart tourism recommender systems 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.LRIT Associated Unit to CNRST (URAC 29), Faculty of SciencesMohammed V UniversityRabatMorocco
  2. 2.SI2M LaboratoryINSEARabatMorocco

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