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.

翻译：推荐系统通常利用多任务学习方法同时优化多个目标，这是基于用户行为数据具有多方面特性。多任务学习的典型做法是在底层建立跨多个任务的参数共享机制，同时在上层为每个任务保留独立的任务塔。由于任务塔直接影响预测结果，我们认为独立任务塔的架构不利于促进正向知识共享。为此，我们提出跨任务塔深度互学习框架，该框架兼容多种主干多任务网络。通过大量离线实验和在线AB测试，评估并验证了所提方法的有效性。