Soohyun Lim

Graph Diffusion Gated Embeddings for Recommender Systems

Modern large-scale recommender systems rely on dense user and item embeddings, but using a full embedding table requires a large number of parameters. To reduce this cost, hashing tricks are commonly used to compress embedding tables. However, collisions and hard bucket assignments can degrade both performance and pers...