  In recommendation systems, the choice of loss function is critical since a
good loss may significantly improve the model performance. However, manually
designing a good loss is a big challenge due to the complexity of the problem.
A large fraction of previous work focuses on handcrafted loss functions, which
needs significant expertise and human effort. In this paper, inspired by the
recent development of automated machine learning, we propose an automatic loss
function generation framework, AutoLossGen, which is able to generate loss
functions directly constructed from basic mathematical operators without prior
knowledge on loss structure. More specifically, we develop a controller model
driven by reinforcement learning to generate loss functions, and develop
iterative and alternating optimization schedule to update the parameters of
both the controller model and the recommender model. One challenge for
automatic loss generation in recommender systems is the extreme sparsity of
recommendation datasets, which leads to the sparse reward problem for loss
generation and search. To solve the problem, we further develop a reward
filtering mechanism for efficient and effective loss generation. Experimental
results show that our framework manages to create tailored loss functions for
different recommendation models and datasets, and the generated loss gives
better recommendation performance than commonly used baseline losses. Besides,
most of the generated losses are transferable, i.e., the loss generated based
on one model and dataset also works well for another model or dataset. Source
code of the work is available at https://github.com/rutgerswiselab/AutoLossGen.
