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Recommender systems usually leverage multi-task learning methods tosimultaneously optimize several objectives because of the multi-faceted userbehavior data. The typical way of conducting multi-task learning is toestablish appropriate parameter sharing across multiple tasks at lower layerswhile reserving a separate task tower for each task at upper layers. Since thetask towers exert direct impact on the prediction results, we argue that thearchitecture of standalone task towers is sub-optimal for promoting positiveknowledge sharing. Accordingly, we propose the framework of Deep MutualLearning across task towers, which is compatible with various backbonemulti-task networks. Extensive offline experiments and online AB tests areconducted to evaluate and verify the proposed approach's effectiveness.

Deep Mutual Learning across Task Towers for Effective Multi-Task Recommender Learning