9780521493369 - Recommender Systems: An Introduction von Dietmar Jannach; Markus Zanker; Alexander Felfernig; Gerhard Friedich (10 Ergebnisse)

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Zustand: New. In.

Zustand: New. 2010. 1st Edition. Hardcover. This book introduces different approaches to developing recommender systems that automate choice-making strategies to provide affordable, personal, and high-quality recommendations. Num Pages: 352 pages, 72 b/w illus. 29 tables. BIC Classification: UDB; UDV. Category: (P) Professional & Vocational. Dimension: 237 x 158 x 24. Weight in Grams: 638. . . . . . Books ship from the US and Ireland.

Hardcover. Zustand: Brand New. 1st edition. 360 pages. 9.00x6.10x1.00 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems. This book offers an overview of approaches to developing state-of-the-art recommender systems that automate a variety of choice-making strategies with the goal of providing affordable, personal, and high-quality recommendations. The authors present algorithmic approaches for generating personalized buying proposals, as well as more interactive and knowledge-based approaches. They discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies.