UserProfile-based personalized research paper recommendation system

This paper proposed UserProfile-based PRPRS (Personalized Research Paper Recommendation System) and an algorithm for extracting keyword is designed and implemented by keyword extraction and keyword inference. Whenever collected research papers by topic are selected, a renewal of user profile increases the frequency of each Domain, Topic and keyword. Each ratio of occurrence is recalculated and reflected on UserProfile. PRPRS calculates the similarity between given topic and collected papers by using Cosine Similarity which is used to recommend initial paper for each topic in Information retrieval. We measured satisfaction and accuracy for each system-recommended paper to test and evaluated performances of the suggested system. Finally PRPRS represents high level of satisfaction and accuracy.