  Recommender systems need to mirror the complexity of the environment they are
applied in. The more we know about what might benefit the user, the more
objectives the recommender system has. In addition there may be multiple
stakeholders - sellers, buyers, shareholders - in addition to legal and ethical
constraints. Simultaneously optimizing for a multitude of objectives,
correlated and not correlated, having the same scale or not, has proven
difficult so far.
  We introduce a stochastic multi-gradient descent approach to recommender
systems (MGDRec) to solve this problem. We show that this exceeds
state-of-the-art methods in traditional objective mixtures, like revenue and
recall. Not only that, but through gradient normalization we can combine
fundamentally different objectives, having diverse scales, into a single
coherent framework. We show that uncorrelated objectives, like the proportion
of quality products, can be improved alongside accuracy. Through the use of
stochasticity, we avoid the pitfalls of calculating full gradients and provide
a clear setting for its applicability.
