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


Tobias Vente, Michael Ekstrand, and Jorean Beel. 2023. Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit. Demo recorded in Proceedings of the 17th ACM Conference on Recommender Systems (RecSys '23). pp. 1212–1216. Proc. RecSys '23. DOI 10.1145/3604915.3610656.


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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