Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems

Recommender system models often represent various sparse features like users, items, and categorical features via embeddings. A standard approach is to map each unique feature value to an embedding vector. The size of the produced embedding table grows linearly with the size of the vocabulary. Therefore, a large vocabulary inevitably lead...More