Dynamic group formation in an online social network

Abstract

• Recommendations for forming small online groups must balance the needs of the host and potential members. • We present a framework of algorithms for making group recommendations in constantly changing problem sets. • We evaluate the framework using a simulated production environment. • We compare our framework’s approximations to an absolute optimal using network flow optimization. For those seeking to recruit teammates for a specific purpose, like a project or study group, challenges quickly arise once they have exhausted their social circle. In the wake of the current pandemic, meeting new people that are right for a specific team is even more difficult than before due to the lack of in-person events. On social media platforms, users often have large networks of connections but have very few close personal relationships within them. This makes it difficult to find compatible people that share the same goal, and are interested in niche groups on those platforms. We present a scalable framework for establishing small online groups that balance two objectives, making the best group recommendations to users and guiding group hosts to the best users for their group. We illustrate this framework using three use cases. Lastly, we evaluate a serverless implementation using a large social media dataset to simulate a production environment and compare our framework to a network flow approach to solving the problem.