Survey on Recommendation Engines built using Collaborative Filtering Techniques

Abstract: With the vast amount of data available today, organizations are looking for more accurate ways of using this data for improving productivity and user experience. Recommender system is one such technology that pro-actively suggests items of interest to users based on their objective behavior on their explicitly stated preferences. Recommendation Engine is one of the most important parts of all commercial and social websites. Whenever a user searches for a book, music, movies or any other product, recommender systems play a huge role in suggesting items that are similar. Recommendations in general are of two types, content based and user based. This paper surveys Recommendation Engines using Collaborative filtering techniques.

Keywords: Recommendation Engines, Collaborative Filtering, Content-Based Filtering, Matrix Factorization