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Scalable Attribute Extraction at Instacart

Published: 15 February 2022 Publication History

Abstract

Structured attribute information extracted from natural text inputs has been extensively exploited in e-commerce to help improve the customer experience. For example, attributes can be extracted from the product catalog data such as product name & product descriptions; similarly, attributes can also be extracted from user queries to the search engine. Having these attribute information available can help greatly boost relevance of many different functionalities such as search, recommendation, and ads. However, with the huge space of product categories and extensive details in the product information, how to extract attribute information from text with high accuracy and high efficiency becomes an extremely challenging problem.
In this talk, we will present the scalable machine learning-based attribute extraction pipeline we have built at Instacart for our online grocery business. We start our presentation with the unique challenges at Instacart on building our meta-catalog (catalog on top of catalogs from different retailers), and how we work with a diverse set of attribute naming conventions from multiple sources. After which we will talk about how we bootstrapped our attribute extraction work from scratch following a human-in-the-loop based solution, and trained our practical machine learning-based attribute extraction solution. We then present our achievement on unifying the attribute extraction on both user search queries and product textual information, and how we tackle the problem of mitigating the vocabulary gap between user search queries and product textual information in the catalog. Finally we present applications of our work in the real production environment and our learnings.

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  • (2024)Technology Development in Online Grocery Shopping—From Shopping Services to Virtual Reality, Metaverse, and Smart Devices: A ReviewFoods10.3390/foods1323395913:23(3959)Online publication date: 8-Dec-2024

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cover image ACM Conferences
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
February 2022
1690 pages
ISBN:9781450391320
DOI:10.1145/3488560
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 February 2022

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Author Tags

  1. deep learning
  2. name entity recognition
  3. natural language processing
  4. semi-supervised learning

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WSDM '22

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Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

View all
  • (2024)Technology Development in Online Grocery Shopping—From Shopping Services to Virtual Reality, Metaverse, and Smart Devices: A ReviewFoods10.3390/foods1323395913:23(3959)Online publication date: 8-Dec-2024

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