Clustering the Sketch: A Novel Approach to Embedding Table Compression

Understand

Embedding tables are used by machine learning systems to work with categorical features.

• In modern Recommendation Systems, these tables can be very large, necessitating the development of new methods for fitting them in memory, even during training.
• We suggest Clustered Compositional Embeddings (CCE) which combines clustering-based compression like quantization to codebooks with dynamic methods like The Hashing Trick and Compositional Embeddings (Shi et al., 2020).
• Experimentally CCE achieves the best of both worlds: The high compression rate of codebook-based quantization, but *dynamically* like hashing-based methods, so it can be used during training.