  A huge amount of user generated content related to movies is created with the
popularization of web 2.0. With these continues exponential growth of data,
there is an inevitable need for recommender systems as people find it difficult
to make informed and timely decisions. Movie recommendation systems assist
users to find the next interest or the best recommendation. In this proposed
approach the authors apply the relationship of user feature-scores derived from
user-item interaction via ratings to optimize the prediction algorithm's input
parameters used in the recommender system to improve the accuracy of
predictions when there are less past user records. This addresses a major
drawback in collaborative filtering, the cold start problem by showing an
improvement of 8.4% compared to the base collaborative filtering algorithm. The
user-feature generation and evaluation of the system is carried out using the
'MovieLens 100k dataset'. The proposed system can be generalized to other
domains as well.
