User-User interaction recommendation, or interaction recommendation, is an indispensable service in social platforms, where the system automatically predicts with whom a user wants to interact. In real-world social platforms, we observe that user interactions may occur in diverse scenarios, and new scenarios constantly emerge, such as new games or sales promotions. There are two challenges in these emerging scenarios: (1) The behavior of users on the emerging scenarios could be different from existing ones due to the diversity among scenarios; (2) Emerging scenarios may only have scarce user behavioral data for model learning. Towards these two challenges, we present KoMen, a Domain Knowledge Guided Meta-learning framework for Interaction Recommendation. KoMen first learns a set of global model parameters shared among all scenarios and then quickly adapts the parameters for an emerging scenario based on its similarities with the existing ones. There are two highlights of KoMen: (1) KoMen customizes global model parameters by incorporating domain knowledge of the scenarios (e.g., a taxonomy that organizes scenarios by their purposes and functions), which captures scenario inter-dependencies with very limited training. (2) KoMen learns the scenario-specific parameters through a mixture-of-expert architecture, which reduces model variance resulting from data scarcity while still achieving the expressiveness to handle diverse scenarios. Extensive experiments demonstrate that KoMen achieves state-of-the-art performance on a public benchmark dataset and a large-scale real industry dataset. Remarkably, KoMen improves over the best baseline w.r.t. weighted ROC-AUC by 2.14% and 2.03% on the two datasets, respectively. Our code is available at: https://github.com/Veronicium/koMen.