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In the big data world, recommendation system is becoming growingly popular. In this work Apache Spark is used to demonstrate an efficient parallel implementation of a new hybrid algorithm for User Oriented Collaborative Filtering method. Dimensionality reduction techniques like Alternating Least Square and Clustering techniques like K-Means are used in order to overcome the limitations of Collaborative Filtering such as data Sparsity and Scalability. We also tried to alleviate the cold start problem of Collaborative Filtering by correlating the users to products through features (tags)

A Hybrid Distributed Collaborative Filtering Recommender Engine Using Apache Spark