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Tourism and Travel sector are improving services through the use of a large amount of data gathered from different sources. The ease access to reviews, rating and experiences from different tourists made the touristic planning rich and complex. Hence, a big challenge faced by tourism sector is to use the gathered data for detecting tourists' preferences and provide personalized itinerary to each particular tourist. In this work, we proposed a solution that is able to detect tourist implicit preferences based on social media photos and recommend a set of tourism attractions. We used emerging techniques such as Convolutional Neural Network and fuzzy logic to classify tourists and perform the recommendation. The proposed approach was integrated to an existing real world smart destination platform and assessed its effectiveness against a ground truth.
Figueredo et al. (Thu,) studied this question.