Building an effective recommender system using machine learning based framework

Machine learning forms the base of many information retrieval applications those effect our day to day lives directly or indirectly. One of the Commonly used application of machine learning algorithms is Recommender Systems. Recommender system are information flitering system which takes users rating for items into account and predict user preferences. Many online ecommerce and other categorical websites are able to generate recommendations either on the basis of implicit feedback or explicit feedback. In implicit feedback, preferences are actually based on analysis of browsing patterns of the user, for example, purchase history, web logs etc. Explicit feedback is generated from the ratings provided by the user. In this paper we have shown adaption of collaborative filtering in Apache Mahout platforms via Eclipse on a sample data set.