Yandex Study: ID Embeddings Outperform GNN Embeddings in Large-Scale Recommenders
_reachsumit · x · 2026-07-30
Yandex published a case study on arXiv comparing item embedding strategies in large-scale recommendation systems, specifically contrasting pretrained GNN-based embeddings with end-to-end trainable ID embeddings.
The research reveals that while a separate pretraining stage benefits low-resource settings with limited training data, it provides no worthwhile benefit for large-scale models trained on extensive datasets, where ID embeddings emerge as the winner.
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