通过嵌套嵌入提升电商搜索效率与精度，推理时压缩12倍。
NEAR$^2$: A Nested Embedding Approach to Efficient Product Retrieval and Ranking
- 用嵌套结构压缩嵌入向量，减少存储与计算开销。
- 在短句、隐含查询上表现更优，精度高于小维度模型。
- 无需额外训练成本，适合大规模实时商品检索场景。
电商平台信息检索系统面临双重挑战：既要精准理解复杂用户查询，又要高效处理海量商品目录。本文提出一种嵌套嵌入方法 NEAR²，可在推理阶段将嵌入尺寸压缩达12倍，且不增加训练成本，同时提升多种基于Transformer的编码器模型在检索与排序任务上的性能。我们在四个不同测试集上验证该方法，采用多负样本排序损失和在线对比损失等多种损失函数，涵盖短文本、隐含查询等多样化检索挑战。结果表明，相比现有模型，NEAR² 在更小嵌入维度下仍保持更高精度，显著提升效率与效果平衡。
原文摘要 · Abstract (English)
E-commerce information retrieval (IR) systems struggle to simultaneously achieve high accuracy in interpreting complex user queries and maintain efficient processing of vast product catalogs. The dual challenge lies in precisely matching user intent with relevant products while managing the computational demands of real-time search across massive inventories. In this paper, we propose a Nested Embedding Approach to product Retrieval and Ranking, called NEAR$^2$, which can achieve up to $12$ times efficiency in embedding size at inference time while introducing no extra cost in training and improving performance in accuracy for various encoder-based Transformer models. We validate our approach using different loss functions for the retrieval and ranking task, including multiple negative ranking loss and online contrastive loss, on four different test sets with various IR challenges such as short and implicit queries. Our approach achieves an improved performance over a smaller embedding dimension, compared to any existing models.
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