A Personalized Paper Recommendation Approach Based on Web Paper Mining and Reviewer´s Interest Modeling

In this article a personalized paper recommendation approach based on the reviewer´s interest model is presented in order to increase the number of reviews for online papers. To achieve this purpose, we first model the reviewer´s interest based on some useful data extracted from the papers in a journal database, such as titles, abstracts, keywords and the Chinese Library Classification Codes (CLCCs). According to the reviewer´s interest model, we then propose a recommendation approach, which can send a paper published online to the reviewers that are experts in the scoop of the paper. Experimental results show that our recommendation approach is effective and achieves 80-90% accuracy in terms of recommending different kinds of papers to the right reviewers.