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arXiv · 2209.05555

An Embedding-Based Grocery Search Model at Instacart

Yuqing Xie·Taesik Na·Xiao Xiao·Saurav Manchanda·Young Rao·Zhihong Xu·Guanghua Shu·Esther Vasiete·Tejaswi Tenneti·Haixun Wang
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Abstract

The key to e-commerce search is how to best utilize the large yet noisy log data. In this paper, we present our embedding-based model for grocery search at Instacart. The system learns query and product representations with a two-tower transformer-based encoder architecture. To tackle the cold-start problem, we focus on content-based features. To train the model efficiently on noisy data, we propose a self-adversarial learning method and a cascade training method. AccOn an offline human evaluation dataset, we achieve 10% relative improvement in RECALL@20, and for online A/B testing, we achieve 4.1% cart-adds per search (CAPS) and 1.5% gross merchandise value (GMV) improvement. We describe how we train and deploy the embedding based search model and give a detailed analysis of the effectiveness of our method.

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Yuqing Xie, Taesik Na, Xiao Xiao, Saurav Manchanda, Young Rao, Zhihong Xu, Guanghua Shu, Esther Vasiete, Tejaswi Tenneti, Haixun Wang. 2022-09-12. An Embedding-Based Grocery Search Model at Instacart. https://arxiv.org/abs/2209.05555

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