Recommender systems are a subclass of information filtering systems that seek to generate meaningful recommendations to users for products or items that might interest them. In recent times, it has become common to collect large amounts of data that allows for a deeper analysis of how a user interacts with the products being offered. Recommender Systems have evolved to fulfill the dual need of buyers and sellers by automating the generation of recommendations based on data analysis. This paper will focus on the extent to which recommender systems are helpful, will compare the various methods used to implement them, problems in collaborative filtering and content based filtering methods and illustrate on some open problems that are common to any recommender system.