  Recommender systems usually leverage multi-task learning methods to
simultaneously optimize several objectives because of the multi-faceted user
behavior data. The typical way of conducting multi-task learning is to
establish appropriate parameter sharing across multiple tasks at lower layers
while reserving a separate task tower for each task at upper layers. Since the
task towers exert direct impact on the prediction results, we argue that the
architecture of standalone task towers is sub-optimal for promoting positive
knowledge sharing. Accordingly, we propose the framework of Deep Mutual
Learning across task towers, which is compatible with various backbone
multi-task networks. Extensive offline experiments and online AB tests are
conducted to evaluate and verify the proposed approach's effectiveness.
