CaMeLS: Cooperative meta-learning service for recommender systems. Wegmeth, L. & Beel, J. In September, 2022. CEUR-WS.
CaMeLS: Cooperative meta-learning service for recommender systems [pdf]Paper  abstract   bibtex   1 download  
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.

Downloads: 1