Neumann Recommender Systems for Information Providers

Designing Customer Centric Paths to Information
1. Auflage 2009
ISBN: 978-3-7908-2134-5
Verlag: Physica-Verlag HD
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 158 Seiten

Reihe: Contributions to Management Science

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.

Autoren/Hrsg.

Neumann, Andreas W.

Inhaltsverzeichnis

1;Preface;5
2;Contents;7
3;1 Introduction;11
3.1;1.1 Recommender Systems;11
3.2;1.2 Scienti.c and Technical Information;14
3.3;1.3 Motivation and Focus;16
3.4;1.4 Chapter Guide;18
4;2 The Market of Scienti.c and Technical Information;19
4.1;2.1 Information Providers in the Digital Age;19
4.2;2.2 The 2-3-6-Value-Chain for STI Markets;24
4.3;2.3 The Strategic Positions of the Market Players;26
4.4;2.4 Recommender Systems in the Business Pro.le of Information Providers;31
5;3 Classi.cation and Mechanism Design of Recommender Systems;32
5.1;3.1 Classi.cations of Recommender Systems;32
5.2;3.2 Mechanism Design;35
6;4 A Survey of Recommender Systems at Major STI Providers;40
6.1;4.1 Scienti.c Libraries;40
6.2;4.2 Scienti.c Projects;44
6.3;4.3 E-Commerce: Amazon.com;45
6.4;4.4 Social Tagging;48
7;5 Case Study: Explicit Recommender Services for Scienti.c Libraries;52
7.1;5.1 General Setup;52
7.2;5.2 Rating Service;56
7.3;5.3 Review Service;57
7.4;5.4 Usage Statistics;59
7.5;5.5 Discussion;60
8;6 General Concepts of Behavior-Based Recommender Services;65
8.1;6.1 Revealed Preference Theory and Choice Sets;65
8.2;6.2 Self-Selection;67
8.3;6.3 Prices, Transaction Costs, Market Baskets, Lending Data, and Browser Sessions;67
8.4;6.4 Knowledge Discovery and Data Mining;69
8.5;6.5 Observed Users vs. Target Group of Recommendations;69
8.6;6.6 Factors for the Di.usion of Recommendations;70
9;7 Algorithms for Behavior-Based Recommender Systems;72
9.1;7.1 Purchase Noise Filtering by Means of the Logarithmic Series Distribution;72
9.2;7.2 POSICI and POMICI: Recommendations from Small Samples;83
9.3;7.3 Related Methods;94
10;8 Case Study: Behavior-Based Recommender Services for Scienti.c Libraries;97
10.1;8.1 Service Description;97
10.2;8.2 Implementation;103
10.3;8.3 Evaluation;112
10.4;8.4 BibTip: Commercial Implicit Recommendation Services for Libraries;121
10.5;8.5 Extensions, Improvements, and Further Applications;122
11;9 Visualizing and Exploring Information Spaces;126
11.1;9.1 A Survey of Visual Interfaces to Information Providers;127
11.2;9.2 RecoDiver: A Graph-Based User Interface to Recommendations;129
11.3;9.3 Evaluation;135
11.4;9.4 Discussion and Outlook;137
12;10 Discussion;139
13;List of Figures;143
14;List of Tables;146
15;References;147