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A collaborative filtering algorithm works by finding a smaller subset of the data from a huge dataset by matching to your preferences. It combines these preferences to create a list of required suggestions. There are several different ways of deciding which people are similar and combining their choices to make a list. Collaborative filtering algorithms have been in use for Recommender systems for a long time now. They have been successful in solving many issues of the systems which are in use in the market. Neighborhood based methods have shown promise in predicting user ratings. In this article we present some of more potential areas of working on Collaborative Filtering technique also we examine approaches for combining multiple algorithms for predicting user ratings and also discuss some results from the analysis of various strategies used by prior researchers and find solutions to them. Keywords-Collaborative Filtering, Movie Ratings Recommender Systems,Similarity.

Analysis of Movie Recommender System using Collaborative Filtering