Graph Diffusion Gated Embeddings for Recommender Systems

Abstract

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 personalization. We introduce Graph Diffusion Gated Embeddings (GDE), which precompute multi-scale, type-separated heat-kernel diffusions from a small set of user/item seeds (via HK-relax) and convert them into degree-corrected probabilistic gating distributions. These gates mix a compact set of learnable seed-cluster embeddings to form node representations. Ablations show that the cross-only variant (cGDE), which uses only cross-type gating, achieves the strongest performance. Across both Top-K retrieval and click through rate prediction tasks, cGDE outperforms hashing-based baseline methods. Our code is available at https://github.com/latent-x/Graph-Diffusion-Gated-Embeddings-for-Recommender-Systems.