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- Author: Andreas W Neumann
- Publisher: Physica Verlag
- Year: 2010
- Pages: 158
- ISBN-10: 3790825786
- ISBN-13: 9783790825787
- Format: 15.6 x 23.4 x 0.9 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
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.
EXTRA 10 % discount with code: EXTRA
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- Author: Andreas W Neumann
- Publisher: Physica Verlag
- Year: 2010
- Pages: 158
- ISBN-10: 3790825786
- ISBN-13: 9783790825787
- Format: 15.6 x 23.4 x 0.9 cm, minkšti viršeliai
- Language: English English
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.
Reviews