Year: 2022
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Xie, Yuqing, Na, Taesik, Xiao, Xiao, Manchanda, Saurav, Rao, Young, Xu, Zhihong, Shu, Guanghua, Vasiete, Esther, Tenneti, Tejaswi, Wang, Haixun
Institutions: University of Waterloo, Instacart (United States)
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4297820935