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Titlebook: Recommender Systems for Information Providers; Designing Customer C Andreas W. Neumann Book 2009 Physica-Verlag Heidelberg 2009 Consumer bu

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书目名称Recommender Systems for Information Providers
副标题Designing Customer C
编辑Andreas W. Neumann
视频video
丛书名称Contributions to Management Science
图书封面Titlebook: Recommender Systems for Information Providers; Designing Customer C Andreas W. Neumann Book 2009 Physica-Verlag Heidelberg 2009 Consumer bu
描述.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..
出版日期Book 2009
关键词Consumer buying behavior; Internet; Libraries; Scientific Information; Web 2; 0; classification; e-commerce
版次1
doihttps://doi.org/10.1007/978-3-7908-2134-5
isbn_softcover978-3-7908-2578-7
isbn_ebook978-3-7908-2134-5Series ISSN 1431-1941 Series E-ISSN 2197-716X
issn_series 1431-1941
copyrightPhysica-Verlag Heidelberg 2009
The information of publication is updating

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Visualizing and Exploring Information Spaces,d by HTML lists or tables into the shop website. The large drawback of this presentation is that the customer is completely unaware of the overall connections between the products. Conceptually, behavior-based recommendations are the weighted edges of a digraph with the products as nodes.
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Classification and Mechanism Design of Recommender Systems,his chapter, different aspects of recommender systems that can serve as building blocks for different types and classifications of systems are determined. Depending on the goal, several classification schemes are possible. A classification based on the type of input data is presented in detail. The
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A Survey of Recommender Systems at Major STI Providers,s at scientific libraries, some of the relevant scientific projects, as well as the most relevant e-commerce application for STI. A comparable analysis can be found in [FGSN08]. While the quantity—not necessarily the quality as well—of recommender systems in commercial applications is quite high, ve
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Case Study: Explicit Recommender Services for Scientific Libraries, of the Universität Karlsruhe (TH). The goal is to turn the online public access catalogs of libraries into customer oriented service portals including the support of customer interaction. In this chapter, the services are described, usage statistics are given, and the question, which type of recomm
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