Now-a-days the data is expanding rapidly in terms of volume, veracity, velocity, variety etc. This exponential growth of data is the result of evolution of social web which in turn led to increase in usage of online services by people to consume and generate data in real time. This growing data is not only structured and semistructured but unstructured as well, which is the main reason behind the lack of useful information from this vast amount of data. So, here arises the difficulty in choosing relevant items of specific category like movies, products, music, books etc., so here comes the Recommender systems with its new and emerging technologies that makes the work of users easy in choosing the most relevant and appropriate among this big data. The heart and source for these recommender systems is the information provided by users in social websites. The techniques implemented in these recommender systems run around the idea of recommending the users based on their taste in preferences, historical information and past activities, on the attributes of user and also on behavioural patterns of all other users. This procedure is known as Collaborative Filtering. This is a mostly used technique which provides recommendations based on the demographic attributes of the users such as age, gender, occupation etc., and also based on the similarity with the other users. This Procedure also uses ratings given by users as feedback which also plays an important role in recommending. So in this project we generate quality recommendations from vast amounts of data using the methodologies of various techniques discussed above. Our recommendations in this project are limited to movies.