Scalable Personalized Recommendation

Do you know that feeling when you dont know which movie to watch? The more movies are in offer the harder it is to find suitable movie. Personalized recommendation systems are trying to fit users needs and give him relevant options to choose from, but there are many things that can influence whats relevant to you in that particular moment, e.g. other users opinion, context, popularity of actors, category…
In our works, we want to determine if quality is relevant for users most of the time. Therefore, we want to see how users are influenced by opinion of experts – in this case – movie critics. We use collaborative filtering approach that boosts or decreases the final estimation of rating based on correlation between user and critic ratings.
Final method should be used only for certain best performing user segments based on algorithm evaluation through multiple segments. Segments can be formed by many features like demography, number of reviews and so on. Combining this with other classic methods can result in robust and scalable movie recommender.