This paper discusses matrix factorization methods that combine different methods including collaborative filtering with the neighborhood models. Since both of these methods focus on different aspects of selection, it would be to ones advantage to combine them and use their strengths and help hide the weaknesses of those algorithms.
This paper did not go into much detail about all of the implementation, it does explain some of the vocabulary used. It also says that whenever a user gives a movie a rating, regardless if it is good or bad, it actually improves the recommendation. This paper also discusses the new neighborhood model and discusses its benefits. We are still in the process of building a recommendation systems and trying to merge different systems that we feel would be best.
http://dl.acm.org/citation.cfm?id=1401944