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: | , |
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Định dạng: | Sách |
Ngôn ngữ: | English |
Được phát hành: |
Springer
2015
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Những chủ đề: | |
Truy cập trực tuyến: | https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/58862 |
Các nhãn: |
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Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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Tóm tắt: | 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. |
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