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

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

Tobias Vente, Michael Ekstrand, Joeran Beel and ACM
Proceedings of the 17th ACM Conference on Recommender Systems, pp 1212-1216
14 Sep 2023
DOI: https://doi.org/10.1145/3604915.3610656
url
https://doi.org/10.1145/3604915.3610656View
Published, Version of Record (VoR) Restricted

Abstract

Information systems -- Information retrieval -- Retrieval tasks and goals -- Recommender systems
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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8 Record Views
5 citations in Web of Science
6 citations in Scopus

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science, Theory & Methods
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