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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Social Networks Based Framework for Recommending Touristic Locations
verfasst von : Mehdi Ellouze, Slim Turki, Younes Djaghloul, Muriel Foulonneau
Erschienen in: Computational Collective Intelligence
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Abstract
Tourists need tools that can help them to select locations in which they can spend their holidays. We have multiple social networks in which we find information about hotels and about users’ experiences. The problem is how tourists can use this information to build their proper opinion about a particular location to decide if they should go to that place or not. We try in this paper to present a design of a solution that can be used to achieve this task. In this paper, we propose a framework for a recommender system that bases on opinions of persons on the one hand and on of users’ preferences on the other hand to generate recommendations. Indeed, opinions of tourists are extracted from different sources and analyzed to finally extract how the hotels are perceived by their customers in terms of features and activities. The final step consists in matching between these opinions and the users’ preferences to generate the recommendations. A prototype was developed in order to show how this framework is really working.