Rajesh Sharma

Bio: Rajesh Sharma is an academic researcher from Government of India. The author has contributed to research in topics: Emerging markets & Agency (sociology). The author has an hindex of 3, co-authored 7 publications receiving 64 citations. Previous affiliations of Rajesh Sharma include Indian Institute of Management Indore.

Developing a roadmap for planning and implementation of interoperability capability in e-government

Abstract: Purpose – The purpose of this paper is to propose a tool in the form of a roadmap for planning and implementation of interoperability capabilities. The issue of interoperability is gaining importance for offering services as “one-stop shop” due to rapid developments in the field of e-government in recent times. Several complexities are associated with this important dimension, and there is an urgent need for developing a tool to facilitate rollout of e-government projects with interoperability capabilities. The proposed roadmap can enable knowledge sharing among key stakeholders for building a common vision and contribute to planning and implementation of interoperability features of e-government. Design/methodology/approach – The paper is an exploratory study using the phenomenographic interpretive approach. The literature review identifies the facets and the issues that are crucial for facilitating interoperability. The status of interoperability in some of the developed countries is reviewed where e-go...

Explanations in recommender systems: an overview

Abstract: Personalisation of product and services is one of the most important aspects in e-commerce and web-based companies. Recommender system is the technology that helps build personalisation features. One promising and emerging area in the field of recommender systems is explanations. Explanation is the reasoning behind the information presented about a recommendation and plays an important role in helping the user evaluate the recommendation received on websites related to e-commerce, social networking, search engines, etc. In this paper, we present an overview of this emerging concept of explanation which is important in present day context as an aid for overcoming information overload. Present status of research in this field is discussed on the three important areas-objectives and evaluation of performance of explanations, effect of explanation interfaces on the users and the growing influence of explanations in social networking sites. We conclude the paper with discussions on areas of future research in this upcoming field which can be of academic interest to the researchers and of practical utility to the system designers of recommender systems for increasing its effectiveness.

Exploring the Impact of Mobile Technology Adoption on Financial Sustainability of Telecentres: The case of India

Abstract: Developing countries, with limited ICT resources available, are heavily dependent upon Telecentres for delivery of e-Government services to citizens, especially in rural areas. As a result, success of e-Governance initiatives in these countries is largely dependent on the financial sustainability of the Telecentres. Technology for delivery of e-Government services is gradually shifting from PC-based model to the mobile-based model. Using exploratory research methodology in context of Common Service Centers in India, this paper examines the impact of this technology shift and aims to understand the issues pertaining to financial sustainability of Telecentres as mobile adoption increases. Our research suggest that it will be difficult for the CSCs to be sustainable in the future and we recommend that government should take steps towards helping the key Telecentres stakeholders develop new capabilities and skills for a new role in the changing environment to retain their relevance and sustainability in the future.

A survey of autoencoder-based recommender systems

Abstract: In the past decade, recommender systems have been widely used to provide users with personalized products and services. However, most traditional recommender systems are still facing a challenge in dealing with the huge volume, complexity, and dynamics of information. To tackle this challenge, many studies have been conducted to improve recommender system by integrating deep learning techniques. As an unsupervised deep learning method, autoencoder has been widely used for its excellent performance in data dimensionality reduction, feature extraction, and data reconstruction. Meanwhile, recent researches have shown the high efficiency of autoencoder in information retrieval and recommendation tasks. Applying autoencoder on recommender systems would improve the quality of recommendations due to its better understanding of users’ demands and characteristics of items. This paper reviews the recent researches on autoencoder-based recommender systems. The differences between autoencoder-based recommender systems and traditional recommender systems are presented in this paper. At last, some potential research directions of autoencoder-based recommender systems are discussed.