Author: Andreas W. Neumann
Publisher: Springer Science & Business Media
Published: 2009-03-03
Total Pages: 160
ISBN-13: 3790821349
DOWNLOAD EBOOKInformation providers are a very promising application area of recommender systems due to the general problem of assessing the quality of information products prior to the purchase. Recommender systems automatically generate product recommendations: customers profit from a faster finding of relevant products, stores profit from rising sales. All aspects of recommender systems are covered: the economic background, mechanism design, a survey of systems in the Internet, statistical methods and algorithms, service oriented architectures, user interfaces, as well as experiences and data from real-world applications. Specific solutions for areas with strong privacy concerns, scalability issues for large collections of products, as well as algorithms to lessen the cold-start problem for a faster return on investment of recommender projects are addressed. This book describes all steps it takes to design, implement, and successfully operate a recommender system for a specific information platform.
Book Synopsis Recommender Systems for Information Providers by : Andreas W. Neumann
Download or read book Recommender Systems for Information Providers written by Andreas W. Neumann and published by Springer Science & Business Media. This book was released on 2009-03-03 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information providers are a very promising application area of recommender systems due to the general problem of assessing the quality of information products prior to the purchase. Recommender systems automatically generate product recommendations: customers profit from a faster finding of relevant products, stores profit from rising sales. All aspects of recommender systems are covered: the economic background, mechanism design, a survey of systems in the Internet, statistical methods and algorithms, service oriented architectures, user interfaces, as well as experiences and data from real-world applications. Specific solutions for areas with strong privacy concerns, scalability issues for large collections of products, as well as algorithms to lessen the cold-start problem for a faster return on investment of recommender projects are addressed. This book describes all steps it takes to design, implement, and successfully operate a recommender system for a specific information platform.