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:  We model this goal with a regularized edge-based clustering objective, and we also show how naive objectives actually have no diversity-experience tradeoff. Although our objective function is NP-hard to optimize, we design an efficient 2-approximation algorithm for optimizing the NP-hard objective, and provide bounds on hyperparameters to avoid trivial solutions .