short-paper

Open access

ROSE: Robust Caches for Amazon Product Search

Authors:

Vihan Lakshman,

Anshumali Shrivastava,

Bing YinAuthors Info & Claims

WWW '22: Companion Proceedings of the Web Conference 2022

Pages 89 - 93

https://doi.org/10.1145/3487553.3524213

Published: 16 August 2022 Publication History

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Abstract

Product search engines like Amazon Search often use caches to improve the customer user experience; caches can improve both the system’s latency as well as search quality. However, as search traffic increases over time, the cache’s ever-growing size can diminish the overall system performance. Furthermore, typos, misspellings, and redundancy widely witnessed in real-world product search queries can cause unnecessary cache misses, reducing the cache’s utility. In this paper, we introduce ROSE, a RObuSt cachE, a system that is tolerant to misspellings and typos while retaining the look-up cost of traditional caches. The core component of ROSE is a randomized hashing schema that makes ROSE able to index and retrieve an arbitrarily large set of queries with constant memory and constant time. ROSE is also robust to any query intent, typos, and grammatical errors with theoretical guarantees. Extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of ROSE. ROSE is deployed in the Amazon Search Engine and produced a significant improvement over the existing solutions across several key business metrics.

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Cited By

Hemmatpour MZheng CZilberman N(2024)E-Commerce Bot Traffic: In-Network Impact, Detection, and Mitigation2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN)10.1109/ICIN60470.2024.10494459(179-185)Online publication date: 11-Mar-2024
https://doi.org/10.1109/ICIN60470.2024.10494459

Index Terms

ROSE: Robust Caches for Amazon Product Search
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

cover image ACM Conferences

WWW '22: Companion Proceedings of the Web Conference 2022

April 2022

1338 pages

ISBN:9781450391306

DOI:10.1145/3487553

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Lionel Médini
Université Lyon 1, France
,
Ivan Herman
W3C / retired

Copyright © 2022 ACM.

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Publication History

Published: 16 August 2022

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WWW '22

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

Virtual Event, Lyon, France

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Cited By

Hemmatpour MZheng CZilberman N(2024)E-Commerce Bot Traffic: In-Network Impact, Detection, and Mitigation2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN)10.1109/ICIN60470.2024.10494459(179-185)Online publication date: 11-Mar-2024
https://doi.org/10.1109/ICIN60470.2024.10494459

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