{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T05:17:31Z","timestamp":1718169451028},"reference-count":12,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2014,8,23]],"date-time":"2014-08-23T00:00:00Z","timestamp":1408752000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["K\u00fcnstl Intell"],"published-print":{"date-parts":[[2014,11]]},"DOI":"10.1007\/s13218-014-0323-2","type":"journal-article","created":{"date-parts":[[2014,8,22]],"date-time":"2014-08-22T08:57:05Z","timestamp":1408697825000},"page":"329-332","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Active Learning for Recommender Systems"],"prefix":"10.1007","volume":"28","author":[{"given":"Rasoul","family":"Karimi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2014,8,23]]},"reference":[{"key":"323_CR1","doi-asserted-by":"crossref","unstructured":"Cohn DA, Jordan M (1995) Active learning with statistical models. In: Proceeding of the advances in neural information processing systems (NIPS)","DOI":"10.21236\/ADA295617"},{"key":"323_CR2","doi-asserted-by":"crossref","unstructured":"Golbandi N, Koren Y, Lempel L (2011) Adaptive bootstrapping of recommender systems using decision trees. In: Proceeding of the WSDM. ACM, New York","DOI":"10.1145\/1935826.1935910"},{"key":"323_CR3","doi-asserted-by":"crossref","unstructured":"Harpale AS, Yang Y (2008) Personalized active learning for collaborative filtering. In: Proceedings of the 31st annual international ACM SIGIR","DOI":"10.1145\/1390334.1390352"},{"key":"323_CR4","unstructured":"Jin R, Si L (2004) A bayesian approach toward active learning for collaborative filtering. In: Proceedings of the 20th conference on UAI"},{"key":"323_CR5","doi-asserted-by":"crossref","unstructured":"Karimi R, Freudenthaler C, Nanopoulosm A, Schmidt-Thieme, L (2011) Non-myopic active learning for recommender systems based on matrix factorization. In: Proceeding of the 12th IEEE international conference on information reuse and integration (IRI), Las Vegas, USA","DOI":"10.1109\/IRI.2011.6009563"},{"key":"323_CR6","doi-asserted-by":"crossref","unstructured":"Karimi R, Freudenthaler C, Nanopoulos A, Schmidt-Thieme L (2011) Towards optimal active learning for matrix factorization in recommender systems. In: Proceeding of the 23th IEEE international conference on tools with artificial intelligence (ICTAI), Florida, USA","DOI":"10.1109\/ICTAI.2011.182"},{"key":"323_CR7","doi-asserted-by":"crossref","unstructured":"Karimi R, Wistuba M, Nanopoulos A, Schmidt-Thieme L (2013) Factorized decision trees for active learning in recommender systems. In: Proceeding of the 25th IEEE international conference on tools with artificial intelligence (ICTAI), Washington DC, USA","DOI":"10.1109\/ICTAI.2013.67"},{"key":"323_CR8","volume-title":"Active learning for recommender systems","author":"R Karimi","year":"2014","unstructured":"Karimi R (2014) Active learning for recommender systems. Cuvillier Verlag, Germany"},{"key":"323_CR9","doi-asserted-by":"crossref","unstructured":"Osugi T, Kun D, Scott S (2005) Balancing exploration and exploitation: a new algorithm for active machine learning. In: IEEE international conference on data mining (ICDM)","DOI":"10.1109\/ICDM.2005.33"},{"key":"323_CR10","doi-asserted-by":"crossref","unstructured":"Rendle S, Schmidt-Thieme L (2008) Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In: Proceeding of the ACM conference on recommender systems (RecSys)","DOI":"10.1145\/1454008.1454047"},{"key":"323_CR11","unstructured":"Schohn G, Cohn D (2000) Less is more: active learning with support vector machines. In: Proceeding of the international conference on machine learning (ICML)"},{"key":"323_CR12","doi-asserted-by":"crossref","unstructured":"Zhou K, Yang SH, Zha H (2011) Functional matrix factorizations for cold-start recommendation. In: Proceedings of 34st annual international ACM SIGIR","DOI":"10.1145\/2009916.2009961"}],"container-title":["KI - K\u00fcnstliche Intelligenz"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13218-014-0323-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13218-014-0323-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13218-014-0323-2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,14]],"date-time":"2019-08-14T05:50:00Z","timestamp":1565761800000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13218-014-0323-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,8,23]]},"references-count":12,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2014,11]]}},"alternative-id":["323"],"URL":"https:\/\/doi.org\/10.1007\/s13218-014-0323-2","relation":{},"ISSN":["0933-1875","1610-1987"],"issn-type":[{"value":"0933-1875","type":"print"},{"value":"1610-1987","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,8,23]]}}}