Three complementary approaches to context aware movie recommendation

     This paper explains three different approaches to Context Aware Movie Recommendation Systems. Each one is a machine learning algorithm. The information they use for their study is a set of movie ratings given by users along with the users favorite actors and genre. They also use information such as gender and age.
    The first method is k-NN classifier. They figure out which other users are most similar to them. They then figure out a number they call "User Movie Interest Value" which indicates how much a person would want to watch that movie and enjoy it. The second method is regression based k-NN in which the main difference from the first one is the way similarity is measured. It is based on an average of two different users for each user/movie pair. The last method is inductive logic programming (ILP). This last approach seems to fit our project more closely because one example they use is inputting the ratings and then set 2 users as friends. Then they could make a rule that friends rate movies similarly. Each method has its own strengths and weaknesses. In order to make a better overall system, one could use all three to get a better recommendation for the user.


http://dl.acm.org/citation.cfm?id=1869652.1869662