Machine Learning Paradigms Applications in Recommender Systems

This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perfo...

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Những tác giả chính:	Lampropoulos, Aristomenis S, Tsihrintzis, George A
Định dạng:	Sách
Ngôn ngữ:	English
Được phát hành:	Springer 2015
Những chủ đề:	Machine learning Artificial intelligence Apprentissage automatique
Truy cập trực tuyến:	https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58862
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spelling	oai:scholar.dlu.edu.vn:DLU123456789-588622023-11-11T06:21:36Z Machine Learning Paradigms Applications in Recommender Systems Lampropoulos, Aristomenis S Tsihrintzis, George A Machine learning Artificial intelligence Apprentissage automatique This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems. 2015-10-16T03:52:52Z 2015-10-16T03:52:52Z 2015 Book 978-3-319-19135-5 https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58862 en application/pdf Springer
institution	Thư viện Trường Đại học Đà Lạt
collection	Thư viện số
language	English
topic	Machine learning Artificial intelligence Apprentissage automatique
spellingShingle	Machine learning Artificial intelligence Apprentissage automatique Lampropoulos, Aristomenis S Tsihrintzis, George A Machine Learning Paradigms Applications in Recommender Systems
description	This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems.
format	Book
author	Lampropoulos, Aristomenis S Tsihrintzis, George A
author_facet	Lampropoulos, Aristomenis S Tsihrintzis, George A
author_sort	Lampropoulos, Aristomenis S
title	Machine Learning Paradigms Applications in Recommender Systems
title_short	Machine Learning Paradigms Applications in Recommender Systems
title_full	Machine Learning Paradigms Applications in Recommender Systems
title_fullStr	Machine Learning Paradigms Applications in Recommender Systems
title_full_unstemmed	Machine Learning Paradigms Applications in Recommender Systems
title_sort	machine learning paradigms applications in recommender systems
publisher	Springer
publishDate	2015
url	https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58862
_version_	1819762729569746944

Machine Learning Paradigms Applications in Recommender Systems

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