Machine Learning Paradigms: Applications in Recommender Systems (Intelligent Systems Reference Library)(Hardcover)
暫譯: 機器學習範式:推薦系統中的應用(智慧系統參考圖書館)(精裝本)
Aristomenis S. Lampropoulos, George A. Tsihrintzis
- 出版商: Springer
- 出版日期: 2015-06-25
- 售價: $4,510
- 貴賓價: 9.5 折 $4,285
- 語言: 英文
- 頁數: 125
- 裝訂: Hardcover
- ISBN: 3319191349
- ISBN-13: 9783319191348
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相關分類:
推薦系統、Machine Learning
海外代購書籍(需單獨結帳)
商品描述
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.
The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and their applications. Finally, the book provides an extended list of bibliographic references which covers the relevant literature completely.
商品描述(中文翻譯)
這本及時的書籍介紹了推薦系統中的應用,這些系統使用機器學習演算法,根據用戶喜歡或不喜歡的內容範例來進行推薦。基於正負範例皆可用的假設所建立的推薦系統,在負範例稀少的情況下表現不佳。正是這個問題,作者在本專著中進行了探討。具體而言,這本書的方法基於最近機器學習研究中出現的一類分類方法。推薦系統與一類分類的結合提供了一個新的、非常肥沃的研究、創新和開發領域,並在「大數據」以及「稀疏數據」問題中具有潛在應用。
這本書對於處理大量和複雜數據問題的研究人員、實務工作者和研究生將非常有用。它適合於模式識別、機器學習和推薦系統領域的專家/研究者,以及希望了解推薦系統及其應用的新興學科的一般讀者。最後,這本書提供了一個擴展的文獻參考列表,全面涵蓋了相關文獻。