So far, initial treatment recommendations for internet-based cognitive behavioral therapy (iCBT) decision support were mostly high-level or static. Personalized treatment recommendations could pave the way toward better treatment outcomes and adaptive treatments by leveraging information from past patients. We explore the disadvantages of multi-class recommendation and propose a modular approach using multilabel classification for treatment recommendations. Our machine learning-based treatment recommender composes treatment programs from a set of modules. It achieves a 79.02% F1-score on historically successful treatments, significantly outperforming the existing system by around 4% while offering other advantages such as interpretability and robustness. Using our recommendation as an initial starting point, clinicians can adjust the modular treatments to provide a more personalized treatment.