Richa Sharma | Chitkara University (original) (raw)

Papers by Richa Sharma

In this work we present RWARS, a novel recommender system that recommends research work area. So ... more In this work we present RWARS, a novel recommender system that recommends research work area. So far a number of recommender systems have been developed in the field of e-commerce, e-services, e-library, entertainment, tourism and social networking sites. However, when it comes to the area of education, not much work has been done. So to extend the utility of Recommender systems in the field of education, we have developed RWARS. We have used Cosine similarity and Tanimoto coefficient for developing our system. The aim of this work is to compare the results obtained using each approach to find the most optimal one. Evaluation parameters that have been used are: Mean square error, Root mean square error and Coverage. At present, RWARS is still in its initial phase and its applicability can be further enhanced by converting it into an online system and it surely will prove to be a great boon for young researchers to select the most appropriate research area for them.

Recommender Systems are often referred to as software tools that help making the selection proces... more Recommender Systems are often referred to as software tools that help making the selection process easier and
time saving. The aim of Recommender Systems is to provide the user with the most suitable recommendation, from the
plethora of options available. Till date, a number of recommender systems have been developed for various application
areas. Although, a lot of work has been done in this particular research area, yet there are still some limitations that need
attention from a researcher’s point of view. In this paper, we present a brief overview of Recommender systems including
their applications, limitations that still need to be worked on and we have also proposed some ideas that can be used to
overcome some of those limitations.

With the advent of Industrial Revolution, not only the choices in various fields increased but al... more With the advent of Industrial Revolution, not only the choices in various fields increased but also the era of computer came into existence thereby revolutionizing the global market. People had numerous choices in front of them that often led to the confusion about what product might actually fulfill their requirements. So the need for having a system which could facilitate the selection criteria and eradicate the dilemma of masses, was realized and ultimately recommender systems of present day world were introduced. So we can refer recommender systems as software tools that narrow down our choices and provide us with the most suitable suggestions as per our requirements. In this paper, we propose a novel recommender system i.e. RWARS (Research Work Area Recommender System) that will recommend research work area to a user based on his/her characteristics similar to those of other users. The characteristics considered here are hobbies, subjects of interests, programming skills and future objectives. The proposed system will use Cosine Similarity approach of Collaborative Filtering.

We propose a novel idea for resolving research issues like cross domain recommendations and recom... more We propose a novel idea for resolving research issues like cross domain recommendations and recommendations
using social networks in the emerging research field recommender systems. Methods/Analysis: According
to this idea user will be recommended with the list of books that belong to the genre that is most liked by the user
in terms of movies. Findings: Here we will collect user’s tastes in movies from his social network profile and extract
out the most liked genre by him and using an appropriate collaborative filtering algorithm will recommend him
with the books that may interest him. Improvement: The proposed idea is expected to resolve research problems
like cold start problem and sparsity. Our proposed methodology gives more competent results than the traditional.

In the last few years recommender systems has grown vigorously as an interesting and new research... more In the last few years recommender systems has
grown vigorously as an interesting and new
research field. Many research articles have been
published in context to the areas like User
Modelling, Information Retrieval and Knowledge
Management etc. that are related to recommender
systems. Most of the research studies in this field
deal with recommending items related to a single
domain (like books, movies etc.). With every new
research there comes a few issues too. This paper
discusses one such issue related to the field of
recommender systems i.e. cross domain
recommendations (basics, tasks, goals etc.)

Recommender systems were introduced in the mid-1990s to help people select the most suitable prod... more Recommender systems were introduced in the mid-1990s to help people select the most suitable product for them from the plethora of options available with them. The idea that led to their development was that we people often rely on the opinions of our peers before trying something new, say it be before buying a smart phone, a laptop, before going for a movie, before going to a new restaurant and even before visiting a doctor. Till date, we have numerous recommender systems developed for various areas, using different recommendation approaches. Yet, there are still a few limitations of recommender systems that need to be worked on. In this paper, we present an overview of recommender systems, the various approaches of recommender systems, the application areas for which various recommender systems have been developed and we also present the limitations of recommender systems.