  Production recommendation systems rely on embedding methods to represent
various features. An impeding challenge in practice is that the large embedding
matrix incurs substantial memory footprint in serving as the number of features
grows over time. We propose a similarity-aware embedding matrix compression
method called Saec to address this challenge. Saec clusters similar features
within a field to reduce the embedding matrix size. Saec also adopts a fast
clustering optimization based on feature frequency to drastically improve
clustering time. We implement and evaluate Saec on Numerous, the production
distributed machine learning system in Tencent, with 10-day worth of feature
data from QQ mobile browser. Testbed experiments show that Saec reduces the
number of embedding vectors by two orders of magnitude, compresses the
embedding size by ~27x, and delivers the same AUC and log loss performance.
