Before revision:  We model this goal with a regularized edge-based  clustering objective,  design an efficient 2-approximation algorithm for optimizing the NP-hard objective, and provide bounds on hyperparameters to avoid trivial solutions .
After revision:  In other words, both diversity and experience are measured from the types of the hyperedges. Our clustering model is based on a regularized version of an edge-based clustering objective, design an efficient 2-approximation algorithm for optimizing the NP-hard objective, and provide bounds on hyperparameters to avoid trivial solutions .