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A Practical Online Allocation Framework at Industry-scale in Constrained Recommendation

Authors:

Hua WuAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 3270 - 3274

https://doi.org/10.1145/3539618.3591835

Published: 18 July 2023 Publication History

Abstract

Online allocation is a critical challenge in constrained recommendation systems, where the distribution of goods, ads, vouchers, and other content to users with limited resources needs to be managed effectively. While the existing literature has made significant progress in improving recommendation algorithms for various scenarios, less attention has been given to developing and deploying industry-scale online allocation system in an efficient manner. To address this issue, this paper introduces an integrated and efficient learning framework in constrained recommendation scenarios at Alipay. The framework has been tested through experiments, demonstrating its superiority over other state-of-the-art methods.

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Online allocation, which is to make sequential decisions in an environment where requests and information arrive in real-time, in order to achieve some optimal effects on the complete timeline, is a crucial challenge, particularly in recommendation systems. For example, the efficient management of limited resources, the distribution of goods, ads, and vouchers. Although significant progress has been made in enhancing recommendation algorithms for various scenarios, industry-scale online allocation systems have received less attention. To tackle this issue, Alipay has introduced an integrated and efficient learning framework for constrained recommendation scenarios. Through thorough experimentation, the framework has demonstrated superior performance compared to other state-of-the-art methods.

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Cited By

Wu YZhu ZMa CQian HLu XZhang YQin XFei BZhou JZhou ABaeza-Yates RBonchi F(2024)Cost-Efficient Fraud Risk Optimization with Submodularity in Insurance ClaimProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672012(3448-3459)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672012

Index Terms

A Practical Online Allocation Framework at Industry-scale in Constrained Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 July 2023

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SIGIR

SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Wu YZhu ZMa CQian HLu XZhang YQin XFei BZhou JZhou ABaeza-Yates RBonchi F(2024)Cost-Efficient Fraud Risk Optimization with Submodularity in Insurance ClaimProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672012(3448-3459)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672012

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