This document discusses building a recommender engine using clustering algorithms like K-Means and MinHash clustering with MapReduce. It provides an introduction to recommender systems and algorithms like collaborative filtering. It describes challenges in building large-scale recommender engines and how Hadoop MapReduce can be used to parallelize recommendation algorithms. The document outlines a proposed system to implement clustering algorithms on MapReduce and evaluate its performance against other frameworks like Apache Mahout using the Netflix dataset.