@inproceedings{wegmeth_camels_2022,
	title = {{CaMeLS}: {Cooperative} meta-learning service for recommender systems},
	url = {https://ceur-ws.org/Vol-3228/paper2.pdf},
	abstract = {We present CaMeLS, a proof of concept of a cooperative meta-learning
service for recommender systems. CaMeLS leverages the computing power of
recommender systems users by uploading their metadata and algorithm
evaluation scores to a centralized environment. Through the resulting
database, CaMeLS then offers meta-learning services for everyone.
Additionally, users may access evaluations of common data sets immediately
to know the best-performing algorithms for those data sets. The metadata
table may also be used for other purposes, e.g., to perform benchmarks. In
the initial version discussed in this paper, CaMeLS implements automatic
algorithm selection through meta-learning over two recommender systems
libraries. Automatic algorithm selection saves users time and computing
power and does not require expertise, as the best algorithm is
automatically found over multiple libraries. The CaMeLS database contains
20 metadata sets by default. We show that the automatic algorithm
selection service is already on par with the single best algorithm in this
default scenario. CaMeLS only requires a few seconds to predict a suitable
algorithm, rather than potentially hours or days if performed manually,
depending on the data set. The code is publicly available on our GitHub
https://camels.recommender-systems.com.},
	urldate = {2022-11-13},
	publisher = {CEUR-WS},
	author = {Wegmeth, Lukas and Beel, Joeran},
	month = sep,
	year = {2022},
}

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