Graph neural networks are the popular deep learning techniques in heterogeneous graph-based recommender systems due to the capability to explore high-order structural information and heterogeneous semantics. Meanwhile, multi-task learning frameworks have been widely adopted in graph representation learning. However, most multi-task-based deep recommendation works mainly suffer from two limitations: interpretability and robustness. In this paper, we propose a customized multi-task learning framework (HMRec) to improve the accuracy and interpretability of recommendations with heterogeneous node classification. We delve into the intricate interplay between recommendation and classification tasks within the co-training framework, crafting a bespoke communication module imbued with adaptive feedback mechanisms to mitigate deleterious interference while enhancing the efficacy of recommendations. Through rigorous experimentation conducted on real-world datasets, we unveil the pronounced superiority of HMRec in comparison to cutting-edge benchmarks.