Recommendation systems are widely used today by online stores and various other leading sites, like Facebook, Instagram and LinkedIn, for providing suggestions to the users. The recommendation process helps the users to find the items that they may be interested in. Also, it is beneficial for the company to improve its overall profit. Recommendation engines use collaborative filtering technique or content-based approach to acquaint the users with such items. As these engines are so beneficial for users as well as for the trading websites, they have already been applied to a large number of fields, such as medical, education, tourism, finance, marketing and business; however, some areas are yet left unexplored. In this paper, we are presenting one such area where if recommendation engines are used, they can help a huge number of researchers around the globe. We propose a recommendation system that can help a number of scholars to get research papers based on the keyword entered by them, and the user will set a similarity index. This value of similarity will help in getting a limited number of papers from a huge pool.
Keywords: Collaborative filtering, Content-based, Recommender systems.
