  As a crucial component of most modern deep recommender systems, feature
embedding maps high-dimensional sparse user/item features into low-dimensional
dense embeddings. However, these embeddings are usually assigned a unified
dimension, which suffers from the following issues: (1) high memory usage and
computation cost. (2) sub-optimal performance due to inferior dimension
assignments. In order to alleviate the above issues, some works focus on
automated embedding dimension search by formulating it as hyper-parameter
optimization or embedding pruning problems. However, they either require
well-designed search space for hyperparameters or need time-consuming
optimization procedures. In this paper, we propose a Single-Shot Embedding
Dimension Search method, called SSEDS, which can efficiently assign dimensions
for each feature field via a single-shot embedding pruning operation while
maintaining the recommendation accuracy of the model. Specifically, it
introduces a criterion for identifying the importance of each embedding
dimension for each feature field. As a result, SSEDS could automatically obtain
mixed-dimensional embeddings by explicitly reducing redundant embedding
dimensions based on the corresponding dimension importance ranking and the
predefined parameter budget. Furthermore, the proposed SSEDS is model-agnostic,
meaning that it could be integrated into different base recommendation models.
The extensive offline experiments are conducted on two widely used public
datasets for CTR prediction tasks, and the results demonstrate that SSEDS can
still achieve strong recommendation performance even if it has reduced 90\%
parameters. Moreover, SSEDS has also been deployed on the WeChat Subscription
platform for practical recommendation services. The 7-day online A/B test
results show that SSEDS can significantly improve the performance of the online
recommendation model.
