Recommender system, an information filtering technology used in many items is presented in web sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media in general. In today's world, time has more value and the researchers have no much time to spend on searching for the right articles according to their research domain. More than 250 research paper recommender systems were published and the quantity of research papers published every day is increasing rapidly. Thus it needs an efficient searching and filtering mechanism to choose the quality research papers, so that the effort and time of researchers can be saved. The recommender system proposed here uses three major factors used for building this system which includes datasets, prediction rating based on users and cosine similarity. The ratings are made by user which will be determined by the number of accurate ratings they provide. The results are then sorted by using cosine similarity. We propose a research-paper recommender system using collaborative filtering approach to recommend a user with best research papers in their domain according to their queries and based on the similarities found from other users on the basis of their queries, which will help in avoiding time consuming searches for the user. © 2019 IEEE.