5 citations · 5 across the 3 of their papers we have counts for
3 papers
Enhancing Cross-Category Learning in Recommendation Systems with Multi-Layer Embedding Training
Zihao Deng, Benjamin Ghaemmaghami, Ashish Kumar Singh +4
Modern DNN-based recommendation systems rely on training-derived embeddings of sparse features. Input sparsity makes obtaining high-quality embeddings for rarely-occurring categori…
Learning to Collide: Recommendation System Model Compression with Learned Hash Functions
Benjamin Ghaemmaghami, Mustafa Ozdal, Rakesh Komuravelli +4
A key characteristic of deep recommendation models is the immense memory requirements of their embedding tables. These embedding tables can often reach hundreds of gigabytes which…
Training with Multi-Layer Embeddings for Model Reduction
Benjamin Ghaemmaghami, Zihao Deng, Benjamin Cho +4
Modern recommendation systems rely on real-valued embeddings of categorical features. Increasing the dimension of embedding vectors improves model accuracy but comes at a high cost…