A Survey on Recommendation System for Future Researchers Using Classifiers

Recommendation system has revolutionized how users and websites communicate, becoming more and more vital. There are numerous uses for recommender systems in a variety of industries, including the economics, education, and science. The amount of digital information is growing swiftly due to the quick growth of information technology. Using big data analysis techniques, researchers use search engines like Google and Bing to find and filter material such as movies, music, and articles. To assist these academics, digital libraries like PubMed, DBLP, and arXiv offer sophisticated search interfaces; however, users must initiate the search process themselves. Therefore, taking into account the general trends of communities and specific interests, a tool is very helpful to actively discover and recommend the most intriguing research articles. The problem of locating the most relevant research publications for a specific academic is looked into. Existing solutions are unable to adequately address the suggestions of new publications since there is a dearth of previous data. As a result, this study on recommendation algorithms include algorithms for collaborative filtering. Additionally, a hybrid recommendation algorithm is created to incorporate real-time smileys, ratings, reviews, and temporal considerations.