  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.
