demonstration

Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit

Authors:

Michael Ekstrand,

Joeran BeelAuthors Info & Claims

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Pages 1212 - 1216

https://doi.org/10.1145/3604915.3610656

Published: 14 September 2023 Publication History

Abstract

LensKit is one of the first and most popular Recommender System libraries. While LensKit offers a wide variety of features, it does not include any optimization strategies or guidelines on how to select and tune LensKit algorithms. LensKit developers have to manually include third-party libraries into their experimental setup or implement optimization strategies by hand to optimize hyperparameters. We found that 63.6% (21 out of 33) of papers using LensKit algorithms for their experiments did not select algorithms or tune hyperparameters. Non-optimized models represent poor baselines and produce less meaningful research results. This demo introduces LensKit-Auto. LensKit-Auto automates the entire Recommender System pipeline and enables LensKit developers to automatically select, optimize, and ensemble LensKit algorithms.

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Cited By

Dietz LSánchez PBellogín A(2025)Understanding the influence of data characteristics on the performance of point-of-interest recommendation algorithmsInformation Technology & Tourism10.1007/s40558-024-00304-0Online publication date: 3-Jan-2025
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Wegmeth LVente TBeel J(2024)Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback DatasetsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691718(1163-1167)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691718
Vente TWegmeth LSaid ABeel J(2024)From Clicks to Carbon: The Environmental Toll of Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688074(580-590)Online publication date: 8-Oct-2024
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Index Terms

Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

September 2023

1406 pages

ISBN:9798400702419

DOI:10.1145/3604915

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Published: 14 September 2023

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RecSys '23: Seventeenth ACM Conference on Recommender Systems

September 18 - 22, 2023

Singapore, Singapore

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Dietz LSánchez PBellogín A(2025)Understanding the influence of data characteristics on the performance of point-of-interest recommendation algorithmsInformation Technology & Tourism10.1007/s40558-024-00304-0Online publication date: 3-Jan-2025
https://doi.org/10.1007/s40558-024-00304-0
Wegmeth LVente TBeel J(2024)Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback DatasetsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691718(1163-1167)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691718
Vente TWegmeth LSaid ABeel J(2024)From Clicks to Carbon: The Environmental Toll of Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688074(580-590)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688074
Wegmeth LVente TPurucker L(2024)Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender SystemsAdvances in Information Retrieval10.1007/978-3-031-56027-9_9(140-156)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56027-9_9

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