An increasing number of media streaming services have expanded their offerings to include entities of multiple content types. For instance, audio streaming services that started by offering music only, now also offer podcasts, merchandise items, and (music) videos. Ranking items across different content types into a single slate poses a significant challenge for traditional learning-to-rank (LTR) algorithms due to differing feature sets and user engagement patterns for different content types. We explore a simple method, called multinomial blending (MB), which can be used in conjunction with most existing LTR algorithms. We compare MB to a range of baselines not only in terms of ranking quality but also from other industry-relevant perspectives such as interpretability, ease-of-use, and stability in dynamic online-learning environments.