Paper recommendation system based on user profile

In recent years, the amount of information has reached enormous dimensions all over the world. Due to reasons such as the development of technology and the increase in the number of academicians, the amount of information has increased excessively all over the world. The excessive increase in the amount of information has made it difficult for researchers to access the correct information. The difficulty in accessing the right information from a large amount of information highlights the importance of recommendation systems. This difficulty is also experienced in the academic environment. Academicians, researchers conduct scientific studies. Scholarly publications are authors and contribute to science. Researchers benefit from the existing knowledge in the literature by examining previously published papers while conducting scientific studies. Academicians and researchers find it challenging to find the most suitable academic articles among them. Article recommendation systems have been developed to reduce this difficulty faced by academics. There are studies in the literature on the recommendation of article. However, traditional article recommendation systems do not take into account the characteristics of the researcher. Classical article recommendation systems recommend the same publications to each researcher. Traditional article recommendation systems compare all articles and present similar articles to the researcher. In the real world, however, researchers have different fields, and expectations. Therefore, it was seen that the article recommendation system should take into consideration the previous publications of the researcher. The method that we propose in this thesis recommends an article specific to the user profile, taking into account the characteristics of the researcher.