Open Access Open Access  Restricted Access Subscription or Fee Access

Discover and Approval System using Big Data and Hadoop Map Reduce

M. Ezhilvendan, K. Kamal Kishore


The very big quantity of data collected more than instant those are difficult to evaluate and feel using general database management implement is now handled by a new technology called big data. The data that are handled using the ability big data are analysed for marketing trends in big business as well as in the sports ground of developed, medication and knowledge.  These types of data contain business transactions, email messages, photos, inspection videos, achievement logs and shapeless text from blogs and population average as well as the huge quantity of information that can be mutually from sensors of all variety. By the method, suggestion Engines have gained a large amount responsiveness in the big data world. Recommender systems are found in various ecommerce applications today. Recommender systems frequently advise the user with a record of recommendations that they might like better, or supply predictions on how much the user power rather both item. Recommendation systems can be developed that considers together the ratings of the customer and the item’s characteristic to advocate the things to the user. It aims at presenting a personalized reference list and recommending the most appropriate items to the users efficiently this system is implemented by using the conception of Hadoop mapreduce. Hadoop is a software formation for distributed processing of large data sets. Hadoop uses Map Reduce paradigm to execute distributed processing over clusters of computers to reduce the time involved in analyzing the item’s feature. The recommender is destined to provide a choice basis of new ideas for customers who call the store more frequently. Recommendations are generated by matching products to customers based on the expected petition of the item for consumption and the preceding expenditure of the customer. We describe a modified recommender scheme deliberate to put advance new products to supermarket shoppers. 


Recommender System, Big Data, Hadoop, Map Reduce.

Full Text:



Atisha Sachan and Vineet Richariya. 2012. “A Survey on Recommender System based on Collaborative Filtering Technique”, International Journal of Innovation in Engineering and Technology (IJIET).

B. Smith, and J. York G. Linden, " Recommendations: Item-to-Item collaborative Filtering," IEEE Internet Computing, vol. 7, no. 1, pp. 76-80, 2003

H. Liang, J. Hogan and Y. Xu. 2010. “Parallel User Profiling Based on Folksonomy for Large Scaled Recommender Systems: An Impli mentation of Cascading MapReduce,” In: Proceedings of the IEEE International Conference on Data Mining Workshops, pp. 156-161.

Ruihai Dongy, Michael P. O’Mahonyz, Markus Schaaly, Kevin McCarthyy, Barry Smyth, Sentimental Product Recommendation, CLARITY: Centre for Sensor Web Technologies RecSys’13, 2013, pp 411-413

G.Suvarna , Context Aware Service Information Extraction From Bigdata Vol. 6 (1) , 2015, 368-373

Wanchun Dou, Xuyun Zhang, Jinjun Chen Shunmei Meng, "KASR: A Keyword-Aware Service Recommendation Method on MapReduce for Big Data Applications," IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, vol. 99, no. 2, 2014.

R. Burke, “Hybrid Recommender Systems: Survey and Experiments,”User Modeling and User-Adapted Interaction, Vol. 12, No.4, pp.331-370, 2002

Z. D. Zhao and M. S. Shang. 2010. "User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop," In: the third International Workshop on Knowledge Discovery and Data Mining, pp. 478- 481.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.