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Modern DNN-based recommendation systems rely on training-derived embeddingsof sparse features. Input sparsity makes obtaining high-quality embeddings forrarely-occurring categories harder as their representations are updatedinfrequently. We demonstrate a training-time technique to produce superiorembeddings via effective cross-category learning and theoretically explain itssurprising effectiveness. The scheme, termed the multi-layer embeddingstraining (MLET), trains embeddings using factorization of the embedding layer,with an inner dimension higher than the target embedding dimension. Forinference efficiency, MLET converts the trained two-layer embedding into asingle-layer one thus keeping inference-time model size unchanged.  Empirical superiority of MLET is puzzling as its search space is not largerthan that of the single-layer embedding. The strong dependence of MLET on theinner dimension is even more surprising. We develop a theory that explains bothof these behaviors by showing that MLET creates an adaptive update mechanismmodulated by the singular vectors of embeddings. When tested on multiplestate-of-the-art recommendation models for click-through rate (CTR) predictiontasks, MLET consistently produces better models, especially for rare items. Atconstant model quality, MLET allows embedding dimension, and model size,reduction by up to 16x, and 5.8x on average, across the models.

Enhancing Cross-Category Learning in Recommendation Systems with Multi-Layer Embedding Training