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An Embedding-Based Grocery Search Model at Instacart. (arXiv:2209.05555v1 [cs.CL])
Sept. 14, 2022, 1:15 a.m. | Yuqing Xie, Taesik Na, Xiao Xiao, Saurav Manchanda, Young Rao, Zhihong Xu, Guanghua Shu, Esther Vasiete, Tejaswi Tenneti, Haixun Wang
cs.CL updates on arXiv.org arxiv.org
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% …
More from arxiv.org / cs.CL updates on arXiv.org
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