Product Recommendations in e-Commerce Retailing Applications.

• Superior document: Forschungsergebnisse der Wirtschaftsuniversitaet Wien.
• Author: Knotzer, Nicolas.
• Place / Publishing House: Frankfurt am Main : : Peter Lang GmbH, Internationaler Verlag der Wissenschaften,, 2007.; ©2008.
• Year of Publication: 2007
• Edition: First edition.
• Language: English
• Series: Forschungsergebnisse der Wirtschaftsuniversität Wien.
• Physical Description: 1 online resource (222 pages)

The book deals with product recommendations generated by information systems referred to as recommender systems. Recommender systems assist consumers in making product choices by providing recommendations of the range of products and services offered in an online purchase environment. The quantitati...

Table of Contents

• Cover
• 1 Introduction
• 1.1 Research Goal
• 1.2 Contents and Organization
• 2 Recommender Systems - Definition, Classification, and Marketing Perspectives
• 2.1 Working Definitions
• 2.2 Classification
• 2.3 Application Models of Recommender Systems
• 2.3.1 Broad Recommendation Lists
• 2.3.2 Customer Comments and Ratings
• 2.3.3 Notification Services
• 2.3.4 Product Associated Recommendations
• 2.3.5 Persistent Personalization
• 2.4 The Consumer Decision Process
• 2.4.1 Need Recognition
• 2.4.2 Information Search
• 2.4.3 Pre-Purchase Evaluation of Alternatives
• 2.4.4 Purchase
• 2.4.5 Post-Purchase Processes
• 2.5 Virtual Communities
• 2.5.1 Characteristics and Benefits
• 2.5.2 Virtual Communities and Network Effects
• 2.5.3 Community Building
• 3 Recommender Systems - Functional Perspectives
• 3.1 Input Data of Recommender Systems
• 3.2 Output Data of Recommender Systems
• 3.3 Measurement Scales for Preference Elicitation
• 3.4 Information Delivery
• 3.5 Recommendation Methods
• 3.5.1 Non-Personalized Recommendation Methods
• 3.5.2 Personalized Recommendation Methods
• 3.5.2.1 Synopsis of Information Filtering Methods
• 3.5.2.2 Human Approaches towards Information Filtering
• 3.5.2.3 Collaborative Filtering
• 3.5.2.4 Attribute-Based Filtering
• 3.5.2.5 Rules-Based Filtering
• 4 Research Model, Hypotheses, and Methodology
• 4.1 Problem Statement
• 4.2 Research Questions and Model
• 4.3 Methodology and Research Design
• 5 Results
• 5.1 Descriptive Results
• 5.1.1 Sample Size and Demographic Data
• 5.1.2 Internet Usage
• 5.1.3 Online Shopping
• 5.1.4 Online Product Recommendations
• 5.1.5 Ratings and Comments
• 5.2 Verification of the Research Model
• 5.2.1 Exploratory Factor Analysis
• 5.2.2 Psychographic Hypotheses - Structural Equation Model
• 5.2.3 Psychographic Hypotheses - Regression Model.
• 5.2.4 Sociodemographic Hypotheses
• 6 Summary and Directions for Further Research
• 6.1 Main Findings
• 6.2 Limitations and Directions for Further Research
• Bibliography
• Appendices
• A AMOS Output
• A.1 Survey AUM
• A.2 Survey AON.