  Multi-task learning (MTL) has been widely used in recommender systems,
wherein predicting each type of user feedback on items (e.g, click, purchase)
are treated as individual tasks and jointly trained with a unified model. Our
key observation is that the prediction results of each task may contain
task-specific knowledge about user's fine-grained preference towards items.
While such knowledge could be transferred to benefit other tasks, it is being
overlooked under the current MTL paradigm. This paper, instead, proposes a
Cross-Task Knowledge Distillation framework that attempts to leverage
prediction results of one task as supervised signals to teach another task.
However, integrating MTL and KD in a proper manner is non-trivial due to
several challenges including task conflicts, inconsistent magnitude and
requirement of synchronous optimization. As countermeasures, we 1) introduce
auxiliary tasks with quadruplet loss functions to capture cross-task
fine-grained ranking information and avoid task conflicts, 2) design a
calibrated distillation approach to align and distill knowledge from auxiliary
tasks, and 3) propose a novel error correction mechanism to enable and
facilitate synchronous training of teacher and student models. Comprehensive
experiments are conducted to verify the effectiveness of our framework in
real-world datasets.
