At Wattpad, the world's leading online storytelling platform, recommendation systems are pivotal to our mission of connecting readers with the stories they love. The Home Page is the primary gateway to Wattpad's diverse content and experiences. As the platform has evolved, we've introduced new content types and classes of stories to meet various business objectives, such as user engagement, merchandising, and marketing. This evolution necessitated recalibrating our homepage recommender system to effectively balance multiple business goals. In this talk, we will discuss how we integrated these objectives into the home recommender stack using probabilistic algorithms derived from the domain of reinforcement learning. Additionally, we will share the challenges we encountered during this transition, such as data sparsity and the cold start problem, along with insights into our development of novel graph neural network architectures tailored for recommendation systems and the new datasets we developed to overcome these hurdles.