There are many activities to do in a city. Usually, a tourist needs a personal guide to know what activities he could like. With Live Recommender we try to help the tourists when they are going to visit a city and they don’t know what activities they would like. Live Recommender is a group of applications to investigate different types of recommendations about tourist activities.

Tourism offers many activities with different features, and it is possible to apply different recommendation algorithms. The main objective is to create a tourist plan for a day or some days. The system calculates the user’s preferences about each type of activity and it displays similar activities.

The branches of research in Live Recommender are:

  • Collaborative tourism:
    This branch investigates the using of a collaborative recommendation. The system saves user’s preferences, and the activities or plans that they have done. We can use this information to recommend activities to other users with similar preferences. Now, the system uses it when it recommends tourism templates.
  • Context recommendation:
    Nowadays, everybody has got a mobile phone or a gadget with internet connection. Thanks these gadgets, our system can obtain information about the context of the user, like weather, time, date, etc., at the moment of the recommendation. The system uses this information to filter or improve the recommendations that the system shows at the user.
  • Non-intrusive recommendation:
    The systems need a lot information about the user to improve him recommendations. A difficulty in this part is that users don’t like doing much forms or writing this information in long forms. For this reason, we are investigating strategies where the system can obtain this information and the user shouldn’t put these data. In this branch, we make an effort to implement shorter tests or delate these tests. To do it, we use other strategies like ELO test, order list or value activities.
  • Social recommendation:
    Normally, the tourism is an activity that people do in group. In addition, people usually belong to a social network, where they share information with their friends. For these reason, we use this social networks to detect the relationship information between each member of the group and we use it to recommend an activity according with the preferences of the group.

Actually, Live Recommender has two applications about tourism recommendations in Madrid:

  • Madrid Live Desktop.
  • Madrid Live Mobile.

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