  Embedding learning for categorical features is crucial for the deep
learning-based recommendation models (DLRMs). Each feature value is mapped to
an embedding vector via an embedding learning process. Conventional methods
configure a fixed and uniform embedding size to all feature values from the
same feature field. However, such a configuration is not only sub-optimal for
embedding learning but also memory costly. Existing methods that attempt to
resolve these problems, either rule-based or neural architecture search
(NAS)-based, need extensive efforts on the human design or network training.
They are also not flexible in embedding size selection or in warm-start-based
applications. In this paper, we propose a novel and effective embedding size
selection scheme. Specifically, we design an Adaptively-Masked Twins-based
Layer (AMTL) behind the standard embedding layer. AMTL generates a mask vector
to mask the undesired dimensions for each embedding vector. The mask vector
brings flexibility in selecting the dimensions and the proposed layer can be
easily added to either untrained or trained DLRMs. Extensive experimental
evaluations show that the proposed scheme outperforms competitive baselines on
all the benchmark tasks, and is also memory-efficient, saving 60\% memory usage
without compromising any performance metrics.
