Advanced Personalized Research Paper Recommendation System Based on Expanded UserProfile through Semantic Analysis

Abstract This paper proposed APRPRS (Advanced Personalized Research Paper Recommendation System) based on UserProfile which is applied keyword expansion through semantic analysis. An algorithm for semantic keyword expansion is designed and implemented. Whenever collected research papers by topic are selected, a renewal of UserProfile increases the frequency of each domain, topic and keyword. Each ratio of occurrence is recalculated and reflected on UserProfile. Semantic keyword expansion extracts the semantically similar keywords that were used in similar papers among including research paper in same domain and adds the extracted keywords to UserProfile. We measured satisfaction and accuracy for each system recommended paper to test and evaluate performances of the suggested keyword expansion method and system. Finally, there are performance improvements about 9% when semantic keyword expansion is applied. As a result of experiment, the suggested system represents high level of satisfaction and accuracy.

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