Abstract
With the rapid growth of scientific publications, researchers need to spend more time searching for papers that align with their research interests. To address this challenge, paper recommendation systems have been developed to help researchers in effectively identifying relevant paper. One of the leading approaches to paper recommendation is content-based filtering method which recommend papers based on the overall similarity of papers. However, studies on user information seeking behaviors indicate that, in addition to evaluating the overall similarity, researchers also pay attention to specific sections of a paper to assess their relevance to their interests. For instance, users may check the method section to determine whether a candidate paper utilize method they are interested in. In this paper, we propose a content-based filtering recommendation method that takes this information seeking behavior into account, aiming to provide users with more relevant papers. Specifically, in addition to considering the overall content of a paper, our approach also considers three specific sections (background, method, and results) and assigns weights to them to better reflect user preferences. We conduct offline evaluations on the DBLP dataset, and the results demonstrate that the proposed method outperforms six baseline methods in terms of precision@5, recall@5, MRR, and MAP.
