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.

About the Author: 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.”

本书全面涵盖了推荐系统的主题，该系统根据用户以前的搜索或购买情况，向用户提供个性化的产品或服务推荐。推荐系统的方法已经适用于各种应用，包括查询日志挖掘、社交网络、新闻推荐和计算广告。本书综合了这一已经达到成熟的研究领域的基础和高级课题。本书的各章分为三类。

--算法和评估。这些章节讨论了推荐系统的基本算法，包括协作过滤方法、基于内容的方法、基于知识的方法、基于集合的方法和评估。

-- 特定领域和背景下的推荐：推荐的背景可以被看作是影响推荐目标的重要侧面信息。探讨了不同类型的背景，如时间数据、空间数据、社会数据、标签数据和可信度。

--高级课题和应用。讨论了推荐系统的各种稳健性问题，如甩锅系统、攻击模型及其防御措施。

此外，还介绍了最新的课题，如学习排名、多臂强盗、群组系统、多标准系统和主动学习系统，并附有应用。

尽管本书主要是作为一本教科书，但由于其对应用和参考文献的关注，它也将吸引工业从业者和研究人员。本书提供了大量的实例和练习，并为教师提供了一份解决方案手册。

关于作者。查鲁-C。Aggarwal是位于纽约州约克敦高地的IBM T.J. Watson研究中心的杰出研究工作人员（DRSM）。他于1993年完成了坎普尔理工学院的学士学位，1996年完成了麻省理工学院的博士学位。他在有参考价值的会议和杂志上发表了300多篇论文，并申请或获得了80多项专利。他是15本书的作者或编辑，包括一本关于数据挖掘的教科书和一本关于离群点分析的综合书。由于其专利的商业价值，他三次被指定为IBM的发明大师。他获得了多个内部和外部奖项，包括EDBT时间测试奖（2014）和IEEE ICDM研究贡献奖（2015）。他还曾担任过许多数据挖掘方面的重要会议的程序或总主席。他是SIAM、ACM和IEEE的研究员，因为 "对知识发现和数据挖掘算法的贡献"。

通过www.DeepL.com/Translator（免费版）翻译