Abstract:
The rapid growth of scientific articles is creating a problem of information overload for the researchers. Due to which both novice and expert researchers find it very difficult to find relevant articles of their interest. Therefore, there is a need for an application that will be able to recommend similar articles to the researcher. To overcome this problem, we will develop a web- based scientific articles recommendations system that will recommend scientific articles of user interest based on text classification Doc2Vec modeling scheme and a similarity measure.
Description:
Web app will take article abstract which is not added yet in the system as input by the user and on clicking generate recommendations your application will infer user article vector. Then it shows articles recommendations by computing cosine similarity between user vector and already added article vectors in descending order.