• Chinnapaga Ravi
• M Bal Raju
• N Subhash Chandra

Many companies are used recommender system to give basic knowledge and feedback information about product and service to the customers. Generate the recommendations based on the likes usage history and preferences. Collaborative filtering generates recommendation by using the previous recommendations of other users. The collaborative filtering is the basic idea for analysis and customer behavior from the past history that gives the future prediction that may like similar items. When process the large amount of data that old and previous recommender techniques are not efficient for sparsity, efficiency and scalability. Single node machines are the time consuming on large data sets. Parallel distributed file filtering model (PDFM), The PDFM recommender system is working on parallel files for MapReduce framework and uses correlation as similarity metric. Parallel distributed file filtering model (PDFM) allows distributed processing of big data across multiple clusters of nodes

"An efficient recommender system approach for collaborative filtering ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.220-224, December-2018, Available :http://www.jetir.org/papers/JETIR1812C32.pdf

"An efficient recommender system approach for collaborative filtering ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp220-224, December-2018, Available at : http://www.jetir.org/papers/JETIR1812C32.pdf