{"_id":"q9GQ34n2jSYkR5ua5","bibbaseid":"wegmeth-beel-camelscooperativemetalearningserviceforrecommendersystems-2022","author_short":["Wegmeth, L.","Beel, J."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","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":[{"propositions":[],"lastnames":["Wegmeth"],"firstnames":["Lukas"],"suffixes":[]},{"propositions":[],"lastnames":["Beel"],"firstnames":["Joeran"],"suffixes":[]}],"month":"September","year":"2022","bibtex":"@inproceedings{wegmeth_camels_2022,\n\ttitle = {{CaMeLS}: {Cooperative} meta-learning service for recommender systems},\n\turl = {https://ceur-ws.org/Vol-3228/paper2.pdf},\n\tabstract = {We present CaMeLS, a proof of concept of a cooperative meta-learning\nservice for recommender systems. CaMeLS leverages the computing power of\nrecommender systems users by uploading their metadata and algorithm\nevaluation scores to a centralized environment. Through the resulting\ndatabase, CaMeLS then offers meta-learning services for everyone.\nAdditionally, users may access evaluations of common data sets immediately\nto know the best-performing algorithms for those data sets. The metadata\ntable may also be used for other purposes, e.g., to perform benchmarks. In\nthe initial version discussed in this paper, CaMeLS implements automatic\nalgorithm selection through meta-learning over two recommender systems\nlibraries. Automatic algorithm selection saves users time and computing\npower and does not require expertise, as the best algorithm is\nautomatically found over multiple libraries. The CaMeLS database contains\n20 metadata sets by default. We show that the automatic algorithm\nselection service is already on par with the single best algorithm in this\ndefault scenario. CaMeLS only requires a few seconds to predict a suitable\nalgorithm, rather than potentially hours or days if performed manually,\ndepending on the data set. The code is publicly available on our GitHub\nhttps://camels.recommender-systems.com.},\n\turldate = {2022-11-13},\n\tpublisher = {CEUR-WS},\n\tauthor = {Wegmeth, Lukas and Beel, Joeran},\n\tmonth = sep,\n\tyear = {2022},\n}\n\n","author_short":["Wegmeth, L.","Beel, J."],"key":"wegmeth_camels_2022","id":"wegmeth_camels_2022","bibbaseid":"wegmeth-beel-camelscooperativemetalearningserviceforrecommendersystems-2022","role":"author","urls":{"Paper":"https://ceur-ws.org/Vol-3228/paper2.pdf"},"metadata":{"authorlinks":{}},"downloads":1},"bibtype":"inproceedings","biburl":"https://api.zotero.org/users/6655/collections/3TB3KT36/items?key=VFvZhZXIoHNBbzoLZ1IM2zgf&format=bibtex&limit=100","dataSources":["HB6fr7bPytW2CAAzC","ca4t6HZh8piBqYaYM","7KNAjxiv2tsagmbgY"],"keywords":[],"search_terms":["camels","cooperative","meta","learning","service","recommender","systems","wegmeth","beel"],"title":"CaMeLS: Cooperative meta-learning service for recommender systems","year":2022,"downloads":1}