Recommender Systems: The Textbook (Paperback)
暫譯: 推薦系統：教科書 (平裝本)

Aggarwal, Charu C.

Recommender Systems: The Textbook (Paperback)
  • 出版商: Springer
  • 出版日期: 2018-04-25
  • 售價: $2,900
  • 貴賓價: 9.5 折 $2,755
  • 語言: 英文
  • 頁數: 498
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 331980619X
  • ISBN-13: 9783319806198
  • 相關分類: 推薦系統
  • 其他版本: Recommender Systems: The Textbook

    • 立即出貨 (庫存=1)

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商品描述

This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories:

Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation.

Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored.

 

Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed.

In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications.

Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.

商品描述(中文翻譯)

這本書全面涵蓋了推薦系統的主題，推薦系統根據用戶之前的搜索或購買行為提供個性化的產品或服務推薦。推薦系統的方法已被應用於多種領域，包括查詢日誌挖掘、社交網絡、新聞推薦和計算廣告。本書綜合了這一研究領域的基本和進階主題，該領域目前已達到成熟階段。本書的章節分為三個類別：

算法與評估：這些章節討論了推薦系統中的基本算法，包括協同過濾方法、基於內容的方法、基於知識的方法、基於集成的方法以及評估。

特定領域和情境中的推薦：推薦的情境可以被視為影響推薦目標的重要附加信息。探討了不同類型的情境，例如時間數據、空間數據、社交數據、標籤數據和可信度。

進階主題與應用：討論了推薦系統的各種穩健性方面，例如虛假系統、攻擊模型及其防禦措施。

此外，還介紹了最近的主題，如學習排序、多臂強盜、群體系統、多標準系統和主動學習系統，並附上應用案例。

雖然這本書主要作為教科書，但由於其對應用和參考的重視，也將吸引業界從業者和研究人員。書中提供了大量的例子和練習，並為教師提供了解答手冊。

作者簡介

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T.J. Watson Research Center in Yorktown Heights, New York. He completed his B.S. from IIT Kanpur in 1993 and his Ph.D. from the Massachusetts Institute of Technology in 1996. He has published more than 300 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 15 books, including a textbook on data mining and a comprehensive book on outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several internal and external awards, including the EDBT Test-of-Time Award (2014) and the IEEE ICDM Research Contributions Award (2015). He has also served as program or general chair of many major conferences in data mining. He is a fellow of the SIAM, ACM, and the IEEE, for "contributions to knowledge discovery and data mining algorithms."

作者簡介(中文翻譯)

Charu C. Aggarwal 是位於紐約約克鎮的 IBM T.J. Watson 研究中心的傑出研究人員 (Distinguished Research Staff Member, DRSM)。他於 1993 年在印度理工學院坎普爾校區獲得學士學位，並於 1996 年在麻省理工學院獲得博士學位。他在經過審核的會議和期刊上發表了超過 300 篇論文，並申請或獲得了超過 80 項專利。他是 15 本書的作者或編輯，包括一本關於資料探勘的教科書和一本全面的異常值分析書籍。由於其專利的商業價值，他三度被 IBM 指定為大師發明家 (Master Inventor)。他獲得了多項內部和外部獎項，包括 EDBT 時間考驗獎 (2014) 和 IEEE ICDM 研究貢獻獎 (2015)。他還擔任過多個資料探勘主要會議的程序或總主席。他是 SIAM、ACM 和 IEEE 的會士，因其對知識發現和資料探勘演算法的貢獻而獲得此榮譽。

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