A personalized information recommendation system for R&D project opportunity finding in big data contexts

With the rapid proliferation of online information, how to find useful information, such as suitable jobs, appropriate experts, and proper projects, is really an important problem. Recommendation technique, as one of emerging tools to deal with information overload and information asymmetry, is critically important for providing personalized online information services. With the increase of R&D investment in government and industry, such as high-tech companies and advanced manufacturing enterprises, more and more R&D project information are launched in public websites for cooperation. When the number of online information and users is extremely huge, how to effectively recommend R&D project opportunities to related researchers and practitioners is a challenging and complex task. In this paper, a novel two-stage method is proposed for R&D project opportunity recommendation. An information filtering method is first offered to identity proper R&D projects as a candidate set. Then, an information aggregation model with various constraints is suggested to recommend appropriate R&D projects for applicants. The proposed method has been implemented in an online research community - ScholarMate (www.scholarmate.com). An online user study has been conducted and the evaluation results exhibit that the proposed method is more effective than existing ones.