An Embedding-Based Grocery Search Model at Instacart

Autor:	Xie, Yuqing, Na, Taesik, Xiao, Xiao, Manchanda, Saurav, Rao, Young, Xu, Zhihong, Shu, Guanghua, Vasiete, Esther, Tenneti, Tejaswi, Wang, Haixun
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted by SIGIR eCom, July 15, 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.05555 Zobrazit plný text záznamu View this record from Arxiv