No abstract

No abstract

No abstract

Nowadays, recommender systems are present in many daily activities such as online shopping, browsing social networks, etc. Given the rising demand for reinvigoration of the tourist industry through information technology, recommenders have been included into tourism websites such as Expedia, Booking or Tripadvisor, among others. Furthermore, the amount of scientific papers related to recommender systems for tourism is on solid and continuous growth since 2004. Much of this growth is due to social networks that, besides to offer researchers the possibility of using a great mass of available and constantly updated data, they also enable the recommendation systems to become more personalised, effective and natural. This paper reviews and analyses many research publications focusing on tourism recommender systems that use social networks in their projects. We detail their main characteristics, like which social networks are exploited, which data is extracted, the applied recommendation techniques, the methods of evaluation, etc. Through a comprehensive literature review, we aim to collaborate with the future recommender systems, by giving some clear classifications and descriptions of the current tourism recommender systems.

Nowadays, social networks are daily used to share what people like, feel, where they travel to, etc. This huge amount of data can say a lot about their personality because it may reect their behaviour from the “real world” to the “virtual world”. Once obtained the access to this data, some authors have tried to infer the personality of the individual without the use of long questionnaires, only working with data in an implicit way, that is, transparently to the user. In this scenario, our work is focused on predicting one of the human personality traits, the Curiosity. In this paper, we analyse the information that can be extracted from the users’ profile on Facebook and the set of features that can be used to describe their degree of curiosity. Finally, we use these data to generate several prediction models. The best generated model is able to predict the degree of curiosity with an accuracy of 87%.