Deep Learning-Based Recommendation System in Tourism by Personality Type Using Social Networks Big Data

Recommendation systems are present in many daily activities. They are trying to predict user preferences. Due to the growth of social networks, there is a vast amount of data that is constantly updated which makes recommendation systems more personalized and efficient. This study aims to apply natural language processing (NLP) and deep learning techniques to obtain a recommendation. NLP is used to analyze the text (i.e. hashtags) from social networks to determine similarity between different points of interest (POI). A pre-trained convolutional neural network (CNN) is used to classify a set of images obtained from social networks to determine which POI is visited by which personality type. The personality type is determined using the Five-Factor (that is, Big Five) model. The Big Five traits are firstly converted into ten personality class labels (High Openness, Low Openness, High Conscientiousness, Low Conscientiousness, High Extraversion, Low Extraversion, High Agreeableness, Low Agreeableness, High Neuroticism, Low Neuroticism) for the classification network. We manually labeled more than 2, 000 images and used a pre- trained CNN in a transfer learning manner to automatically extract features from images and classify them. We demonstrated that personality traits can be extracted from posted images with an accuracy of 75%. Also, we showed that those traits can be aggregated for a given set of pictures, such that a representation of a destination can be determined.