research-article

Microsoft Recommenders: Best Practices for Production-Ready Recommendation Systems

Authors:

Andreas Argyriou,

Miguel González-Fierro,

Le ZhangAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 50 - 51

https://doi.org/10.1145/3366424.3382692

Published: 20 April 2020 Publication History

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Abstract

Recommendation algorithms have been widely applied in various contemporary business areas, however the process of implementing them in production systems is complex and has to address significant challenges. We present Microsoft Recommenders, an open-source Github repository for helping researchers, developers and non-experts in general to prototype, experiment with and bring to production both classic and state-of-the-art recommendation algorithms. A focus of this repository is on best practices in development of recommendation systems. We have also incorporated learnings from our experience with recommendation systems in production, in order to enhance ease of use; speed of implementation and deployment; scalability and performance.

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Cited By

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Akhadam AKbibchi OMekouar LIraqi Y(2025)A Comparative Evaluation of Recommender Systems ToolsIEEE Access10.1109/ACCESS.2025.354101413(29493-29522)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3541014
Moreschini SLenarduzzi VCoba LCodabux ZGalster MSpinola R(2024)Towards a Technical Debt for AI-based Recommender SystemProceedings of the 7th ACM/IEEE International Conference on Technical Debt10.1145/3644384.3648574(36-39)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3644384.3648574
Li JLi HHe ZMa WSun PZhang MMa S(2024)ReChorus2.0: A Modular and Task-Flexible Recommendation LibraryProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688076(454-464)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688076
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Index Terms

Microsoft Recommenders: Best Practices for Production-Ready Recommendation Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications
2. Software and its engineering

Index terms have been assigned to the content through auto-classification.

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Information

Published In

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 20 April 2020

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SIGWEB

WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Akhadam AKbibchi OMekouar LIraqi Y(2025)A Comparative Evaluation of Recommender Systems ToolsIEEE Access10.1109/ACCESS.2025.354101413(29493-29522)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3541014
Moreschini SLenarduzzi VCoba LCodabux ZGalster MSpinola R(2024)Towards a Technical Debt for AI-based Recommender SystemProceedings of the 7th ACM/IEEE International Conference on Technical Debt10.1145/3644384.3648574(36-39)Online publication date: 14-Apr-2024
https://dl.acm.org/doi/10.1145/3644384.3648574
Li JLi HHe ZMa WSun PZhang MMa S(2024)ReChorus2.0: A Modular and Task-Flexible Recommendation LibraryProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688076(454-464)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688076
Hu HHan KYin ZLian D(2024)Spatial and Temporal User Interest Representations for Sequential RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.337845411:5(6087-6097)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3378454
Lai YZhu YPan BZhou K(2024)Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00241(2958-2976)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00241
Haddou KAkdim IMekouar LIraqi Y(2024)Beyond Positive Similarity Metrics: Leveraging Negative Co-Occurrence in Recommender SystemsIEEE Access10.1109/ACCESS.2024.348396612(154212-154229)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3483966
Al Jurdi WBou Abdo JDemerjian JMakhoul A(2024)Exploring the Power of Weak Ties on Serendipity in Recommender SystemsComplex Networks & Their Applications XII10.1007/978-3-031-53503-1_17(205-216)Online publication date: 29-Feb-2024
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Jia JLiu YHu YGong NCalandrino JTroncoso C(2023)POREProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620333(1703-1720)Online publication date: 9-Aug-2023
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Bajenaru VLavoie SBenyo BRiker CColby MVaccaro J(2023)Recommender System Metaheuristic for Optimizing Decision-Making ComputationElectronics10.3390/electronics1212266112:12(2661)Online publication date: 14-Jun-2023
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Hidasi BCzapp Á(2023)The Effect of Third Party Implementations on ReproducibilityProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3609487(272-282)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3609487
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Abstract

References

Cited By

Index Terms

Recommendations

A survey of serendipity in recommender systems

New Recommendation Techniques for Multicriteria Rating Systems

Improving recommendation accuracy based on item-specific tag preferences

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