Leveraging Content and Connections for Scientific Article Recommendation in Social Computing Contexts

Rapid proliferation of information technologies has generated a great volume of information that makes scientific information searching more challenging. Personalized recommendation is a widely used technique to help researchers find relevant information. Researchers involved in a social computing context generate abundant content and form heterogeneous connections. Existing article recommendation techniques fail to perform a deep analysis of this information. This research proposes a novel approach to recommend scientific articles to researchers by leveraging content and connections. In this approach, we first analyze the semantic content of the article by keyword similarity calculation and then extract online users' connections to support article voting and finally employ a two-stage recommendation process to suggest relevant articles. The proposed method has been implemented in ScholarMate (www.scholarmate.com), an online research social network platform. Two experiments are conducted and the evaluation results indicate that the proposed method is more effective than the baseline methods.