Recommender Systems: The Textbook (Paperback)

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.

本書全面介紹了推薦系統的主題，該系統根據用戶的先前搜索或購買行為，為用戶提供個性化的產品或服務推薦。推薦系統方法已被適應到包括查詢日誌挖掘、社交網絡、新聞推薦和計算廣告等各種應用中。本書綜合了一個已經成熟的研究領域的基礎和高級主題。本書的章節分為三個類別：

算法和評估：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、基於集成的方法和評估方法。

特定領域和上下文中的推薦：推薦的上下文可以被視為影響推薦目標的重要附加信息。本書探討了不同類型的上下文，如時間數據、空間數據、社交數據、標籤數據和可信度數據。

高級主題和應用：本書討論了推薦系統的各種韌性方面，如操縱系統、攻擊模型及其防禦措施。

此外，本書還介紹了最新的主題，如學習排序、多臂搶錢機、群體系統、多標準系統和主動學習系統，並提供了相應的應用。

儘管本書主要作為教材，但由於其對應用和參考文獻的關注，也將吸引工業從業人員和研究人員。書中提供了大量的例子和練習，並且教師可以獲得解答手冊。

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."

Charu C. Aggarwal是IBM T.J. Watson Research Center位於紐約約克鎮高地的傑出研究員。他於1993年從印度理工學院卡納普爾分校獲得學士學位，並於1996年從麻省理工學院獲得博士學位。他在被審查的會議和期刊上發表了300多篇論文，並申請或獲得了80多項專利。他是15本書的作者或編輯，包括一本關於數據挖掘的教科書和一本全面介紹異常值分析的書籍。由於他專利的商業價值，他三次被IBM指定為大師發明家。他獲得了多個內部和外部獎項，包括EDBT Test-of-Time Award（2014年）和IEEE ICDM Research Contributions Award（2015年）。他還擔任過許多重要的數據挖掘會議的程序或總召集人。他是SIAM、ACM和IEEE的會士，以表彰他在知識發現和數據挖掘算法方面的貢獻。