对比工业级图神经网络与端到端训练的物品嵌入，发现大数据下前者无优势
Embedding Items at Scale: Comparing GNN-Based and ID-Based Item Embeddings in the Yandex Ecosystem
- 用预训练图神经网络和端到端学习两种方式生成物品嵌入
- 小数据集上预训练嵌入效果更好，大规模数据下二者性能接近
- 适用于推荐系统工程师评估嵌入策略的成本与收益
基于Transformer的序列推荐模型依赖于物品嵌入策略。现有方法或使用预训练物品嵌入，或与Transformer端到端联合训练。据我们所知，尚无研究在大规模工业场景中从成本与质量双角度比较这两种方法。本文以俄罗斯Yandex平台的两个成熟推荐系统——Yandex Market与Yandex Music为例，开展案例研究，对比预训练工业级图神经网络物品嵌入与端到端可训练嵌入的表现。此外，还在一个从Yandex Lavka生产日志中采样的低资源数据集上评估了两种方法，相关数据与代码已公开。结果表明，在训练数据有限时，预训练阶段有帮助；但在大规模模型、海量数据训练下，预训练带来的提升不显著。
原文摘要 · Abstract (English)
Transformer-based sequential recommendation models, which process sequences of user-item interactions, rely heavily on the item embedding strategy. Existing approaches either use pretrained item embeddings or learn them end-to-end with the transformer. To the best of our knowledge, no prior work has compared these options from both cost and quality perspectives in a large-scale industrial setting. This paper is a case study that compares pretrained industrial graph neural network item embeddings with end-to-end trainable item embeddings across two mature production recommendation systems at Yandex: Yandex Market and Yandex Music. We additionally evaluate both approaches on a low-resource dataset sampled from Yandex Lavka production logs, for which both the data and code are publicly available for demonstration purposes. Our results show that a separate pretraining stage helps when training data is limited, but provides no worthwhile benefit for large-scale models trained on extensive datasets.
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