  Continuous representations have been widely adopted in recommender systems
where a large number of entities are represented using embedding vectors. As
the cardinality of the entities increases, the embedding components can easily
contain millions of parameters and become the bottleneck in both storage and
inference due to large memory consumption. This work focuses on post-training
4-bit quantization on the continuous embeddings. We propose row-wise uniform
quantization with greedy search and codebook-based quantization that
consistently outperforms state-of-the-art quantization approaches on reducing
accuracy degradation. We deploy our uniform quantization technique on a
production model in Facebook and demonstrate that it can reduce the model size
to only 13.89% of the single-precision version while the model quality stays
neutral.
