Raghavendra C K

Work place: Department of Computer Science & Engineering, BNM Institute of Technology, Bengaluru, 560070, Karnataka, India

E-mail: raghav.ck.clk@gmail.com


Research Interests: Social Computing, Computer systems and computational processes, Image Compression, Image Manipulation, Image Processing, Data Mining, Data Structures and Algorithms


Raghavendra C K is currently working as an Assistant Professor in the Department of Computer Science and Engineering at B N M Institute of Technology, Bangalore and pursuing the Ph.D degree in the Department of Computer Science and Engineering at S J B Institute of Technology, Bangalore under Visvesvaraya Technological University, Belagavi, India. He obtained his B.E degree in 2009 and M.Tech degree in 2011 from Visvesvaraya Technological University, Belagavi, India. His current research interests are in the area of Data Mining, Web Mining, Big Data Analytics, Recommendation systems Social Networks Analysis and Image Processing.

Author Articles
Personalized Recommendation Systems (PRES): A Comprehensive Study and Research Issues

By Raghavendra C K Srikantaiah K.C Venugopal K. R

DOI: https://doi.org/10.5815/ijmecs.2018.10.02, Pub. Date: 8 Oct. 2018

The type of information systems used to recommend items to the users are called Recommendation systems. The concept of recommendations was seen among cavemen, ants and other creatures too. Users often rely on opinion of their peers when looking for selecting something, this usual behavior of the humans, led to the development of recommendation systems. There exist various recommender systems for various areas. The existing recommendation systems use different approaches. The applications of recommendation systems are increasing with increased use of web based search for users’ specific requirements. Recommendation techniques are employed by general purpose websites such as google and yahoo based on browsing history and other information like user’s geographical locations, interests, behavior in the web, history of purchase and the way they entered the website.
Document recommendation systems recommend documents depending on the similar search done previously by other users. Clickstream data which provides information like user behavior and the path the users take are captured and given as input to document recommendation system. Movie recommendation systems and music recommendation systems are other areas in use and being researched to improve. Social recommendation is gaining the momentum because of huge volume of data generated and diverse requirements of the users. Current web usage trends are forcing companies to continuously research for best ways to provide the users with the suitable information as per the need depending on the search and preferences.
This paper throws light on common strategies being followed for building recommendation systems. The study compares existing techniques and highlights the opportunities available for research in this area.

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