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.