Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems
WWW '20: The Web Conference 2020 Taipei Taiwan April, 2020, pp. 562-566, 2020.
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
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