Dr. Chhavi Rana

Maharshi Dayanand University, UIET, Faculty Member

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Scholar and Teacher of Contemporary Web Technologies and Digital Revolution algorithms.

Dr. Chhavi directs the Computer Science Engineering Department at University Institute of Engineering and Technology, Maharshi Dayanand University in Haryana, India. She is an Assistant Professor and she is head of the department of computer science. Dr. Chhavi is currently researching in the area of Information and communication Technologies for Sustainable Development. She holds a Ph.D. from NIT, Kurukshetra, India, an Institute of National Importance accredited by Government of India and has been serving in the Computer Science Engineering Department at Maharshi Dayanand University, Haryana in India since 2007. She is also head of the Internet Technology and Development group at the Internet Technology and Development Lab.

My research focuses on information and communication technologies for social, economic, and political development. In particular I study the internet-enabled services and their design, impact, and importance particularly in India. I research engineering, public policy, hci/usability and sustainability issues with reference to machine learning and information retrieval capabilities as well as methods to assess and evaluate social, economic, and political development outcomes. I am also interested in the impact of information and communication technologies on the development-security nexus and in post-conflict peace and reconciliation.

I am also interested in simple lifestyle changes that makes life better for our fellow human beings and creating an open and free thinking environment through communication to enhance productivity and personal satisfaction.

My motto of life is SILRFT that stands for simple innovative living by raising free thinking.
Supervisors: Sanjay K. Jain
Phone: 9896308747
Address: Model Town, Rohtak, Haryana, India.

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Journal Papers by Dr. Chhavi Rana

An extended evolutionary clustering algorithm for an adaptive Recommender System

The usage of internet and Web services has tremendously increased since the past decade. This usa... more The usage of internet and Web services has tremendously increased since the past decade. This usage has made life easier in many respects, especially in finding the required information with the help of search engines. However, the problem of finding relevant information still persists primarily due to an increasing number of choices presented by the search engines. As recommender systems (RSs) are evolving, these new generation RSs are adopted by many web services to build long-term relation with customers. RSs aid users in finding relevant information on the web. Several techniques deal with this problem and data mining is widely used among them. They target the information overload problem and also strive to present updated suggestions as new information arrives. The goal of presenting updated information to users is one of the foremost challenge in the area of RS research as user’s interests also keeps on changing with time together with system data. This paper addresses the problem of user requirements changing over a period of time in seeking information on web and how RSs deal with them. We propose a Dynamic Recommender system based on evolutionary clustering algorithm that preludes the widely used matrix factorization techniques in RS. This clustering algorithm makes clusters of similar users and evolves them depicting accurate and relevant user preferences over time. Particularly, the approach proposes an optimization function that uses temporal parameters in a clustering algorithm for attaining the accurate evolution of user interest in the form of clusters. The new approach is empirically tested and compared with other clustering algorithm depicting considerable improvement in the quality of clusters that in turn can be used for predicting user changing preferences in a RS.

An evolutionary clustering algorithm based on temporal features for dynamic Recommender Systems

An Evolutionary clustering algorithm for handling dynamics of user profile in Recommendation systems

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A study of dynamic features of recommender system

The extensive usage of internet is fundamentally changing the way we live and communicate. Conseq... more The extensive usage of internet is fundamentally changing the way we live and communicate. Consequently, the requirements of users while browsing internet are changing drastically. Recommender Systems (RSs) provide a technology that helps users in finding relevant contents on internet. Revolutionary innovations in the field of internet and their consequent effects on users have activated the research in the area of recommender systems. This paper presents issues related to the changing needs of user requirements as well as changes in the systems’ contents. The RSs involving said issues are termed as Dynamic Recommender Systems (DRSs). The paper first defines the concept of DRS and explores the various parameters that contribute in developing a DRS. The paper also discusses the scope of contributions in this field and concludes citing in possible extensions that can improve the dynamic qualities of recommendation systems in future.

Books by Dr. Chhavi Rana

Recommender Systems: Search Engine for Future

Rana Chhavi, “Recommender Systems: Search Engine for Future”, Data Mining, Amazon Digital South A... more

Prediction Function with Time Factor in Collaborative Filtering

Singroha Dolly, Rana Chhavi , “Prediction Function with Time Factor in Collaborative Filtering”, ... more

Khatri Meghna, Rana Chhavi, “Similarity Function with Temporal Factor in Collaborative Filtering”... more

Talks by Dr. Chhavi Rana

"Web Usage mining"

Conference Presentations by Dr. Chhavi Rana

Digital Technologies and Developing Countries: Prospects, Potential and Challenges

Rana, C. and Jain, S.K., “Digital Technologies and Developing Countries: Prospects, Potential and... more

A Comparative study of prediction user preferences using evolutionary clustering algorithm

Rana, C. and Jain, S.K., “A Comparative study of prediction user preferences using evolutionary c... more

A Study of Faculty Development Programs In India To Improve Student Engagement

ACSME Conference, University of Sydney, September, 2014.

Performance Parameter for evaluation in E-commerce Recommender Systems

Rana, C. and Jain, S.K., “Performance Parameter for evaluation in E-commerce Recommender Systems”... more Rana, C. and Jain, S.K., “Performance Parameter for evaluation in E-commerce Recommender Systems” DMDMS : International Workshop On Data Mining For Decision Making Support , In Conjunction With The 7th International Conference On Ambient Systems, Networks And Technologies (Ant-2016) Madrid, Spain, 2016.

A Recommendation Model for Handling Dynamics in User Profile

Recommender System has become the necessary agent for a naive user in the information bombardment... more Recommender System has become the necessary agent for a naive user in the information bombardment arena of World Wide Web. In the last decade, World Wide Web emerged as an all encompassing technology that is revolutionizing the way people live. With the passage of time, user behaviors evolve and as such should be the recommendations provided. There exists a wide gap in literature to cater effectively with the issue of temporal evolution of data on the internet. This paper will specifically analyze various ways through which temporal issue can be handled in generating user profile that evolve with time. It will develop a recommendation model to handle the dynamics in user profile. The prime focal point is to examine if recommendation accuracy can be improved by adding temporal dimension. An empirical study is carried out to compare the analysis of the traditional data mining tasks and the proposed method.