As the spread of science and the number of researchers working in academic fields increase, there is also a considerable increase in the number of academic studies. Researchers always follow new works published for keeping their knowledge up to date. However, with many different sources and thousands of academic publications published every day, academics are not always able to find publications about their subjects. Today, almost all of online academic databases employ a recommendation module which only considers the studies similar to the paper that the user looked at. However, a recommendation system based on the information of a single article is often not enough. In this study, the proposed method recommends by considering user's publications, user’s co-authors and co-authors’ papers. Therefore, meta-data of the articles published by the researcher in the past are scored on a time-base basis with the method we propose. With the help of the sum of scores, there is a score of the user profile in the subject matter. It aims to find the closest studies to the profile of the user by searching with the method propsoed in the data pool which we created from the exact contents of hundreds of thousands of academic works. In the proposed method, TF-IDF is used from frequency-based similarity analysis methods. In the evaluation phase, the performance of the proposed method was examined. The success test of the method was measured by several different methods. These are to be evaluated by presenting them to real users and the other is to compare with existing data. The results are very promising and demonstrate that the method can produce accurate and quality results.
Article recommendation system; profile based recommendation system; TF-IDF