A Keyword-based Scholar Recommendation Framework for Biomedical Literature

With the development of modern technology, more and more research papers have been published and shared in various digital databases. However, it is time-consuming for researchers to find suitable scholars who study the same research field with them. To address this issue, we focus on proposing a keyword-based scholar recommendation framework, which can help users to advance their research by recommending scholars that align with the users interests. We first utilize keywords that are extracted from abstract to construct a word-word co-occurrence graph in each query. Based on the graph, we propose an approach to find core nodes to solve cold start problem by treating these core nodes as the users interests. We then combine these core nodes and authors to build a bipartite graph, and adopted the PersonalRank algorithm to rank authors based on the bipartite graph. Finally, we design a recommendation evaluation criterion by comparing our recommendation lists with the results lists of Microsoft Academic search. Experimental results show that our recommendation framework can effectively and efficiently generate scholar recommendation in the situation that lack of the citations times, user behavior and other important indicators.