Our recommendation strategies predict the rating that each user would assign to every item in the catalogue. Finally, the products with the highest prediction is proposed. In our approach we use a content-based individual recommender that compares each item to be proposed with items already rated by the user (in the Preferences Test).
One requirement for this service is to have available the current movie listing of a selected city. We obtain this through our Web Crawler.
User firstly have to choose a city:
The best five movies for the selected city will appear as the individual recommendations:
Users can always look at the details of the recommended movies: