Abstract—In this fast trending technological era, data is growing very fast all around the globe. Today by analyzing large data sets, one can spot business trends, detect environmental changes, predict forthcoming social agendas and combat crime. This so-called "Big Data” analytics is challenging and dependent on time complexity. Amidst all this, we also know that space is becoming lesser day by day. In a time when computer imaging and web browsing demand the use of many different data, it is very important for us to be able to store all the types in reasonably sized matrices. A database is like a matrix but due to the growing data it will have a never ending addition of rows. To tackle this situation, we have tried to compare some matrix compression techniques that will help in efficient and fast working of analytic web recommendation systems and will decrease the effort of information retrieval from large data sets.

Index Terms—Big Data, data mining, image processing,information retrieval, matrix factorization, recommendation systems, time complexity.

Animesh Pandey is with the Department of Information Technology, and Siddharth Shrotriya is with the department of Electronic and Communication Engineering, Jaypee Institute of Information Technology, A- 10,Sector-62,Noida,UP, India (e-mail: animeshpandey@acm.org andsiddharth@ieee.org )

Cite: Animesh Pandey and Siddharth Shrotriya, "Comparing the Effect of Matrix Factorization Techniques in Reducing the Time Complexity for Traversing the Big Data of Recommendation Systems," International Journal of Computer and Communication Engineering vol. 2, no. 2, pp. 170-173 , 2013.