In complex tasks (beyond a single targeted controller) requiring robots to collaborate with multiple human users, two challenges arise: complex tasks are often composed of multiple behaviors which can only be evaluated as a collective (a meta-behavior) and user preferences often differ between individuals, yet successful interactions are expected across groups. To address these challenges, we formulate a set-wise preference learning problem, and validate a cost function that captures human group preferences for complex collaborative robotic tasks (cobotics). We develop a sparse optimization formulation to introduce a distinctiveness metric that aggregates individuals with similar preference profiles. Analysis of anonymized unlabelled preferences provides further insight into group preferences. Identification of the mode average most-preferred meta-behavior and minimum covariance bound allows us to analyze group cohesion. A user study with 43 participants is used to validate group preference profiles.