To keep pace with increased applications of recommender systems, collaborative filtering algorithms have played a major role in providing better and accurate recommendations to the users. Their performance in providing the top results, that actually help the users, has also improved over the previous years. Collaborative Filtering (CF) algorithms are used in the social media sites as well as in the personalized recommender systems for the users and deal with problems like cold start, data sparsity, information overload, synonymy etc. Here, the recommendation is based on the preferences of user`s friends or the user`s own past preferences. This paper gives a detailed review of the algorithms used by various recommender system that are based on collaborative filtering. It investigates the algorithms based on their input parameters, their performance and various other factors of importance.

Collaborative Filtering, Social Media, Folksonomy, Personalized Ranking, Data Sparsity, Tagging, User Similarity

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