  Designing an effective loss function plays a crucial role in training deep
recommender systems. Most existing works often leverage a predefined and fixed
loss function that could lead to suboptimal recommendation quality and training
efficiency. Some recent efforts rely on exhaustively or manually searched
weights to fuse a group of candidate loss functions, which is exceptionally
costly in computation and time. They also neglect the various convergence
behaviors of different data examples. In this work, we propose an AutoLoss
framework that can automatically and adaptively search for the appropriate loss
function from a set of candidates. To be specific, we develop a novel
controller network, which can dynamically adjust the loss probabilities in a
differentiable manner. Unlike existing algorithms, the proposed controller can
adaptively generate the loss probabilities for different data examples
according to their varied convergence behaviors. Such design improves the
model's generalizability and transferability between deep recommender systems
and datasets. We evaluate the proposed framework on two benchmark datasets. The
results show that AutoLoss outperforms representative baselines. Further
experiments have been conducted to deepen our understandings of AutoLoss,
including its transferability, components and training efficiency.
