Recommender systems have become very popular in day to day life. Various e-commerce websites, social network sites and web search use vast usage of recommender engines and various algorithms to find out the user profiles of customers, thereby use those profiles to find similar users. With the advancement of World Wide Web, there arose a constant need to connect people from different parts of the world in various ways possible. It called for efforts to provide techniques that can bring forth common interests among users. Recommender system is such a system wherein information is gathered based on the preferences of the users and item data sets. These sources of information provide output, which can help users to predict and recommend it to other users. The implementation of RS has increased in the internet, which explained its use in diverse areas like recommending movies, applications, gadgets, songs, websites, research and discussion forums etc. where user can give ratings to an item of his choice; thus creating a rating matrix. © Research India Publications.
K. B. Yedugiri, Chandni, S., Sini Raj P., and Souparnika, S., “Recommender systems - A deeper insight”, International Journal of Applied Engineering Research, vol. 9, pp. 28521-28531, 2014.