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 and efficiently 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 in terms of big data analytics and MapReduce framework. An information filtering method is first offered to identity proper R&D projects as a candidate set. Then, a multi-objective programming 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). The proposed method is an alternative and promising tool for online information recommendation services.