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Query Attribute Recommendation at Amazon Search

Published: 13 September 2022 Publication History

Abstract

Query understanding models extract attributes from search queries, like color, product type, brand, etc. Search engines rely on these attributes for ranking, advertising, and recommendation, etc. However, product search queries are usually short, three or four words on average. This information shortage limits the search engine’s power to provide high-quality services.
In this talk, we would like to share our year-long journey in solving the information shortage problem and introduce an end-to-end system for attribute recommendation at Amazon Search. We showcase how the system works and how the system contributes to the long-term user experience through offline and online experiments at Amazon Search. We hope this talk can inspire more follow-up works in understanding and improving attribute recommendations in product search.

Supplementary Material

MP4 File (Query attribute recommendation at Amazon Search.mp4)
The video presentation of the paper Query Attribute Recommendation at Amazon Search

References

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Hiteshwar Kumar Azad and Akshay Deepak. 2019. A new approach for query expansion using Wikipedia and WordNet. Information sciences 492(2019), 147–163.
[2]
Yi Chang and Hongbo Deng. 2020. Query understanding for search engines. Springer.
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Xin Luna Dong, Xiang He, Andrey Kan, Xian Li, Yan Liang, Jun Ma, Yifan Ethan Xu, Chenwei Zhang, Tong Zhao, Gabriel Blanco Saldana, 2020. AutoKnow: Self-driving knowledge collection for products of thousands of types. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2724–2734.
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Hanqing Lu, Yunwen Xu, Qingyu Yin, Tianyu Cao, Boris Aleksandrovsky, Yiwei Song, Xianlong Fan, and Bing Yin. 2021. Unsupervised Synonym Extraction for Document Enhancement in E-commerce Search. (2021).
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Chen Luo, Vihan Lakshman, Anshumali Shrivastava, Tianyu Cao, Sreyashi Nag, Rahul Goutam, Hanqing Lu, Yiwei Song, and Bing Yin. 2022. ROSE: Robust Caches for Amazon Product Search. (2022).
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Parikshit Sondhi, Mohit Sharma, Pranam Kolari, and ChengXiang Zhai. 2018. A Taxonomy of Queries for E-commerce Search. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 1245–1248.
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Danqing Zhang, Zheng Li, Tianyu Cao, Chen Luo, Tony Wu, Hanqing Lu, Yiwei Song, Bing Yin, Tuo Zhao, and Qiang Yang. 2021. QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 4362–4372.
[8]
Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, and Xueqi Cheng. 2020. Query Understanding via Intent Description Generation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1823–1832.
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Wen Zhang, Yanbin Lu, Bella Dubrov, Zhi Xu, Shang Shang, and Emilio Maldonado. 2021. Deep Hierarchical Product Classification Based on Pre-Trained Multilingual Knowledge. (2021).

Cited By

View all
  • (2024)Combating Missed Recalls in E-commerce Search: A CoT-Prompting Testing ApproachCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663842(220-231)Online publication date: 10-Jul-2024
  • (2024)COSMO: A Large-Scale E-commerce Common Sense Knowledge Generation and Serving System at AmazonCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3653398(148-160)Online publication date: 9-Jun-2024
  • (2024)Exploring Query Understanding for Amazon Product Search2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10826015(2343-2348)Online publication date: 15-Dec-2024
  • Show More Cited By

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    cover image ACM Other conferences
    RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems
    September 2022
    743 pages
    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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    Publication History

    Published: 13 September 2022

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    Author Tags

    1. Attribute Recommendation
    2. Product Search
    3. Query Understanding

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    View all
    • (2024)Combating Missed Recalls in E-commerce Search: A CoT-Prompting Testing ApproachCompanion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering10.1145/3663529.3663842(220-231)Online publication date: 10-Jul-2024
    • (2024)COSMO: A Large-Scale E-commerce Common Sense Knowledge Generation and Serving System at AmazonCompanion of the 2024 International Conference on Management of Data10.1145/3626246.3653398(148-160)Online publication date: 9-Jun-2024
    • (2024)Exploring Query Understanding for Amazon Product Search2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10826015(2343-2348)Online publication date: 15-Dec-2024
    • (2024)Cross-view hypergraph contrastive learning for attribute-aware recommendationInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10370161:4Online publication date: 18-Jul-2024
    • (2022)Improving the Accuracy of Recommendation Systems based on the Relations in Knowledge Graph2022 4th International Academic Exchange Conference on Science and Technology Innovation (IAECST)10.1109/IAECST57965.2022.10061920(978-982)Online publication date: 9-Dec-2022

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