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Demo

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

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Tobias Vente (University of Siegen), Michael Ekstrand (Boise State University) and Joeran Beel (University of Siegen).

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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 65.5% (19 out of 29) 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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