A Deep Hierarchical Ensemble Network (DHEN) is proposed for predicting conversion rates in ad-recommendation systems, addressing challenges such as feature-crossing module selection, model depth and width, and hyper-parameter tuning. The authors introduce a multitask learning framework utilizing DHEN, enhance prediction through user behavior sequences, and implement a self-supervised auxiliary loss to tackle label sparseness, achieving state-of-the-art performance in CVR prediction.