Social Recommendation With Large-Scale Group Decision-Making for Cyber-Enabled Online Service

Along with the development of several emerging computing paradigms and information communication technologies, it is said that cyber computing technology is playing an increasingly important role across cyber-related systems and applications. In this article, we focus on cyber-social computing and propose a computational model that integrates large-scale group decision-making (LSGDM) into social recommendations for cyber-enabled online services. As a concrete application example, a graph model is built to describe the LSGDM problem among researchers in scholarly big data environments. Following the basic profiling to describe decision-makers within scholarly networks, measures are defined to evaluate one researcher's academic performance and research outcome and further quantify correlations between them based on their collaboration relationships in a constructed network model. A two-stage large-scale decision-making solution is then proposed for social recommendations: A network partition algorithm is developed based on the identification of experts along with their influence extending to a group of researchers, and a random walk with the restart-based algorithm is improved to calculate the weighted decisions for group decision aggregation and alternative ranking. Experiments using the real-world data demonstrate the usefulness and effectiveness of our proposed model and method, which can provide the target researcher with more reliable recommendations.