Lightweight Embeddings with Graph Rewiring for Collaborative Filtering

GNN-based recommender systems have become increasingly popular in academia and industry due to their ability to capture high-order information from user-item interaction graphs. However, as recommendation services scale rapidly and their deployment now commonly involves resource-constrained edge devices, GNN-based models face significant challenges, including high embedding storage costs and run-time latency from graph propagations. Our previous work, LEGCF, effectively reduced embedding storage costs but struggled to maintain recommendation performance under stricter storage limits. Additionally, LEGCF did not address the extensive run-time computation costs associated with graph propagation, which involves heavy multiplication and accumulation operations (MACs). These challenges consequently hinder effective training and inference on resource-constrained edge devices. To address these limitations, we propose Lightweight Embeddings with Rewired Graph (LERG) for Graph Collaborative Filtering, an improved extension of LEGCF. LERG retains LEGCF’s compositional codebook structure but introduces quantization techniques to reduce the storage cost of embedding weights, enabling the inclusion of more meta-embeddings within the same storage constraints for improved model expressiveness. To optimize graph propagation for edge devices, we pretrain the quantized compositional embedding table using the full interaction graph on resource-rich servers, after which a fine-tuning stage is engaged to identify and prune low-contribution entities via a gradient-free binary integer programming approach, constructing a rewired graph that excludes these entities (i.e., user/item nodes) from propagating signals. The quantized compositional embedding table with selective embedding participation and sparse rewired graph is transferred to edge devices which significantly reduce computation memory and inference time. Experiments on three public benchmark datasets, including an industry-scale dataset, demonstrate that LERG achieves superior recommendation performance while dramatically reducing storage and computation costs for graph-based recommendation services.