Hybrid Movie Recommenders based on neural networks and decision trees

The internet provides a lot of information to users. To help users find the items of their interest in this information overload, recommender systems have been developed. In this thesis we explored movie recommender systems based on three recommendation methods: content-based, collaborative filtering and a hybrid recommendation one based on the previous two. The algorithms that we used are the decision tree learning and the neural networks. The algorithms were implemented by using the data mining software Weka. To test these recommender systems, we combined the movie data from the Internet Movie Database and the rating data provided by Netflix. The results show that the proposed hybrid recommender systems does not perform better or worse than the content-based recommender systems and collaborative filtering recommender systems.