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@inproceedings{vente_introducing_2023,
	address = {New York, NY, USA},
	series = {{RecSys} '23},
	title = {Introducing {LensKit}-{Auto}, an experimental automated recommender system ({AutoRecSys}) toolkit},
	isbn = {9798400702419},
	url = {https://dl.acm.org/doi/10.1145/3604915.3610656},
	doi = {10.1145/3604915.3610656},
	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.},
	urldate = {2023-09-18},
	booktitle = {Proceedings of the 17th {ACM} {Conference} on {Recommender} {Systems}},
	publisher = {Association for Computing Machinery},
	author = {Vente, Tobias and Ekstrand, Michael and Beel, Joeran},
	month = sep,
	year = {2023},
	keywords = {Algorithm Selection, AutoRecSys, Automated Recommender Systems, CASH, Hyperparameter Optimization, Recommender Systems},
	pages = {1212--1216},
}