Abstract
There are number of users and items in any type of recommender system. There are numerous information on internet and so many visitors on websites which add some challenges for generating recommender system. A recommender system extracts the user preferences or interests from the related data sets so there is low information overload. Therefore, new recommendation system is required which will provide more quality recommendations for huge data sets. So, for these types of issues we have discovered several techniques of recommendation techniques which are three types such as: Content-based filtering, Collaborative filtering and Hybrid filtering. This paper also analyzes different algorithms in each type of recommender system.
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