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Automating recommender systems experimentation with librec-auto

Published: 27 September 2018 Publication History

Abstract

Recommender systems research often requires the creation and execution of large numbers of algorithmic experiments to determine the sensitivity of results to the values of various hyperparameters. Existing recommender systems platforms fail to provide a basis for systematic experimentation of this type. In this paper, we describe librec-auto, a wrapper for the well-known LibRec library, which provides an environment that supports automated experimentation.

References

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Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Gaël Varoquaux. 2013. API design for machine learning software: experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning. 108--122.
[2]
Michael D. Ekstrand, Michael Ludwig, Jack Kolb, and John T. Riedl. 2011. LensKit: a modular recommender framework. In Proceedings of the fifth ACM conference on Recommender systems. 349--350.
[3]
Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2011. My Media Lite: a free recommender system library. In Proceedings of the fifth ACM conference on Recommender systems. 305--308.
[4]
Guibing Guo, Jie Zhang, Zhu Sun, and Neil Yorke-Smith. 2015. LibRec: A Java Library for Recommender Systems. In UMAP Workshops.
[5]
Carlos E. Seminario and David C. Wilson. 2012. Case Study Evaluation of Mahout as a Recommender Platform. In RUE@ RecSys. 45--50.

Cited By

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  • (2022)CD-SemMF: Cross-Domain Semantic Relatedness Based Matrix Factorization Model Enabled With Linked Open Data for User Cold Start IssueIEEE Access10.1109/ACCESS.2022.317556610(52955-52970)Online publication date: 2022
  • (2021)librec-auto: A Tool for Recommender Systems ExperimentationProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482006(4584-4593)Online publication date: 26-Oct-2021
  • (2021)User-centered Evaluation of Popularity Bias in Recommender SystemsProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456821(119-129)Online publication date: 21-Jun-2021
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Published In

cover image ACM Conferences
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems
September 2018
600 pages
ISBN:9781450359016
DOI:10.1145/3240323
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: 27 September 2018

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Author Tags

  1. experimentation
  2. librec
  3. recommender systems frameworks

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  • Demonstration

Conference

RecSys '18
Sponsor:
RecSys '18: Twelfth ACM Conference on Recommender Systems
October 2, 2018
British Columbia, Vancouver, Canada

Acceptance Rates

RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all
  • (2022)CD-SemMF: Cross-Domain Semantic Relatedness Based Matrix Factorization Model Enabled With Linked Open Data for User Cold Start IssueIEEE Access10.1109/ACCESS.2022.317556610(52955-52970)Online publication date: 2022
  • (2021)librec-auto: A Tool for Recommender Systems ExperimentationProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482006(4584-4593)Online publication date: 26-Oct-2021
  • (2021)User-centered Evaluation of Popularity Bias in Recommender SystemsProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456821(119-129)Online publication date: 21-Jun-2021
  • (2021)Flatter Is BetterACM Transactions on Intelligent Systems and Technology10.1145/343791012:2(1-16)Online publication date: 9-Mar-2021
  • (2020)The Connection Between Popularity Bias, Calibration, and Fairness in RecommendationProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3418487(726-731)Online publication date: 22-Sep-2020
  • (2020)Fairness-aware Recommendation with librec-autoProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3411525(594-596)Online publication date: 22-Sep-2020
  • (2020)FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender SystemsProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3394860(154-162)Online publication date: 7-Jul-2020
  • (undefined)Break Out of a Pigeonhole: A Unified Framework for Examining Miscalibration, Bias, and Stereotype in Recommender SystemsACM Transactions on Intelligent Systems and Technology10.1145/3650044

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