  Practical large-scale recommender systems usually contain thousands of
feature fields from users, items, contextual information, and their
interactions. Most of them empirically allocate a unified dimension to all
feature fields, which is memory inefficient. Thus it is highly desired to
assign different embedding dimensions to different feature fields according to
their importance and predictability. Due to the large amounts of feature fields
and the nuanced relationship between embedding dimensions with feature
distributions and neural network architectures, manually allocating embedding
dimensions in practical recommender systems can be very difficult. To this end,
we propose an AutoML based framework (AutoDim) in this paper, which can
automatically select dimensions for different feature fields in a data-driven
fashion. Specifically, we first proposed an end-to-end differentiable framework
that can calculate the weights over various dimensions for feature fields in a
soft and continuous manner with an AutoML based optimization algorithm; then we
derive a hard and discrete embedding component architecture according to the
maximal weights and retrain the whole recommender framework. We conduct
extensive experiments on benchmark datasets to validate the effectiveness of
the AutoDim framework.
