Research Work Area Recommendation based on Collaborative Filtering

Abstract

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.

IoT Applications for Recommended Methods of Physical Education Online Course Resources Based on Collaborative Filtering Technology

“…Most of this data pertains to online shopping platforms. The bulk of this data makes it tough to analyze and According to Sharma et al [6], RWARS, a new recommender system for recommending research areas, is introduced in this paper. E-commerce, e-services, e-libraries, entertainment, tourism, and social networking sites all have recommender systems in place.…”