通过改进用户历史建模，显著降低推荐系统推理延迟。
Efficient user history modeling with amortized inference for deep learning recommendation models
- 将候选项附加到历史末尾并用交叉注意力融合，替代传统拼接方式。
- 该方法使推断延迟降低30%，且推荐效果与拼接法相当。
- 适合大规模推荐系统部署，尤其关注推理效率的场景。
我们研究了在深度学习推荐模型（DLRM）中使用Transformer编码器进行用户历史建模。此类架构虽能显著提升推荐质量，但通常带来高延迟，需升级基础设施或采用极小模型。用户历史建模中的关键问题是候选项的早期融合，已有多种方法被探索。本文重新审视早期融合，比较将候选项与每个历史项拼接，与将其作为独立项附加至历史末尾的方式。采用后一种方法，可对最近提出的渐进式历史推断算法M-FALCON —— 进行重构，适用于DLRM模型。实验表明，附加并结合交叉注意力的方法性能与拼接法相当，且渐进化推断大幅降低推理成本。最终在LinkedIn Feed和Ads场景部署结果显示，相较于非渐进化推断，该方法将延迟降低30%。
原文摘要 · Abstract (English)
We study user history modeling via Transformer encoders in deep learning recommendation models (DLRM). Such architectures can significantly improve recommendation quality, but usually incur high latency cost necessitating infrastructure upgrades or very small Transformer models. An important part of user history modeling is early fusion of the candidate item and various methods have been studied. We revisit early fusion and compare concatenation of the candidate to each history item against appending it to the end of the list as a separate item. Using the latter method, allows us to reformulate the recently proposed amortized history inference algorithm M-FALCON \cite{zhai2024actions} for the case of DLRM models. We show via experimental results that appending with cross-attention performs on par with concatenation and that amortization significantly reduces inference costs. We conclude with results from deploying this model on the LinkedIn Feed and Ads surfaces, where amortization reduces latency by 30\% compared to non-amortized inference.
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