Abstract Growth of technology and innovation leads to large and complex data which is coined as Bigdata. As the quantity of information increases, it becomes more difficult to store and process data. The greater problem is finding right data from these enormous data. These data are processed to extract the required data and recommend high quality data to the user. Recommender system analyses user preference to recommend items to user. Problem arises when Bigdata is to be processed for Recommender system. Several technologies are available with which big data can be processed and analyzed. Hadoop is a framework which supports manipulation of large and varied data. In this paper, a novel approach Cosine Neighbourhood Similarity measure is proposed to calculate rating for items and to recommend items to user and the performance of the recommender system is evaluated under different evaluator which shows the proposed Similarity measure is more accurate and reliable.

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[24] Wikipedia, http://en.wikipedia.org/wiki/Recommender_syst em, Last Visited 2014. Kola Sujatha Periyasamy is a Senior Assistant Professor in Madras Institute of Technology of Anna University, India. She received her M.C.A. in Computer Applications in 1999, M.E in Computer Science and Engineering in 2003 and her Ph.D degree in Computer Science and Engineering in 2013 from College of Engineering, Guindy, Anna University, India. She has 11 years teaching experience in the branch of Information Technology. Her current research focuses on Data Mining and Big Data Analytics. Jayadharini Jaiganesh completed her Bachelor Degree in Information Technology in Madras Institute of Technology, Anna University, India. Her areas of interests are data mining and image processing. She has published a paper IEEE Conference. She is pursuing her research in the area of data mining. Kanchan Kumar Ponnambalam completed his Bachelor Degree in Information Technology in Madras Institute of Technology, Anna University, India. He is well versed in Photoshop. His areas of interests are data mining and Operating Systems. Jeevitha Rajasekar completed her Bachelor Degree in Information Technology in Madras Institute of Technology, Anna University, India. Her areas of interests are data mining and soft computing. 754 The International Arab Journal of Information Technology, Volume 14, No. 5, September 2017 Kannan Arputharaj is a Professor and Head of the Department of Information Science and Technology, College of Engineering, Anna University, India. He received his B.Sc. degree in Mathematics from Madurai Kamaraj University in 1979, his Master degree in Mathematics from Annamalai University in 1986, his M.E degree in Computer Science and Engineering from College of Engineering, Guindy, Anna University, India in 1991 and his Doctorate in Intelligent Temporal Databases from the Faculty of Electrical Engineering, Anna University, India in 2000. He worked as a Programmer in Bhabha Atomic Research Centre (BARC), Mumbai, India from 1981 to 1989. He has published numerous papers in various International journals and conferences. He has 27 years of teaching experience. His area of research includes Database Management System, Artificial Intelligence, Big Data Analytics, Data Mining and Software Engineering.