In the era of Internet, web is a giant source of information. The constantly growing rate of information in the web makes people confused to decide which product is relevant to them. To find relevant product in today’s era is very time consuming and tedious task. Everyday a lot of information is uploaded and retrieved from the web. The web is overloaded with information and it is very essential to cop up with this overloaded and overlooked information. Recommender systems are the solution which can help a user to get relevant information from the bulk of information. Recommender systems provide customized or personalized and non personalized recommendations to interested users. Recommender systems are in its evolution stage. Recommender systems have been evolved from first generation to third generation through second generation. First generation or Web 1.0 recommender systems deal with E-commerce, Second generation or web 2.0 recommender systems use social network and social contextual information for accurate and diverse recommendations, and Third generation recommender systems use location based information or internet of things for generating recommendations. In this paper, three generation of recommender systems and are discussed. Similarity measures and evaluation metrics are used in these generations are also discussed.

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.