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  • 1
    Online Resource
    Online Resource
    Product Recommendations in e-Commerce Retailing Applications. (2007)
    Frankfurt a.M. :Peter Lang GmbH, Internationaler Verlag der Wissenschaften,
    Details
    UID:
    almahu_9949568478602882
    Format: 1 online resource (222 pages)
    Edition: 1st ed.
    ISBN: 9783631754528
    Series Statement: Forschungsergebnisse der Wirtschaftsuniversitaet Wien Series ; v.17
    Note: 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.
    Additional Edition: Print version: Knotzer, Nicolas Product Recommendations in e-Commerce Retailing Applications Frankfurt a.M. : Peter Lang GmbH, Internationaler Verlag der Wissenschaften,c2007 ISBN 9783631566220
    Language: English
    Keywords: Electronic books. ; Hochschulschrift
    URL: Click to View
    URL: kostenfrei
    URL: lizenzpflichtig
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    Stabi Berlin
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  • 2
    Book
    Book
    Product recommendations in e-commerce retailing applications (2008)
    Frankfurt am Main [u.a.] : Lang
    Details
    UID:
    b3kat_BV036105687
    Format: XX, 202 S. , Ill., graph. Darst.
    ISBN: 9783631566220
    Series Statement: Forschungsergebnisse der Wirtschaftsuniversität Wien 17
    Note: Zugl.: Wien, Univ., Diss., 2006
    Language: English
    Subjects: Economics
    RVK:
    QP 650
    RVK:
    QR 760
    Keywords: Electronic Commerce ; Buchhandel ; Empfehlungssystem ; Verbraucherverhalten ; Hochschulschrift
    URL: Inhaltsverzeichnis
    Author information: Knotzer, Nicolas
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  • 3
    Online Resource
    Online Resource
    Product Recommendations in E-Commerce Retailing Applications (2018)
    Frankfurt a.M. : Peter Lang GmbH, Internationaler Verlag der Wissenschaften
    Details
    UID:
    almahu_9948168572302882
    Format: 1 online resource
    Edition: 1st, New ed.
    ISBN: 9783631754528
    Series Statement: Forschungsergebnisse der Wirtschaftsuniversität Wien 17
    Content: 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 quantitative research study investigates the influence of psychographic and sociodemographic determinants on the interest of consumers in personalized online book recommendations. The author presents new findings regarding the interest in recommendations, importance of product reviews for the decision process, motives for submitting ratings as well as comments, and the delivery of recommendations. The results show that opinion seeking, opinion leading, domain specific innovativeness, online shopping experience, and age are important factors in respect of the interest in personalized recommendations.
    Note: Doctoral Thesis , Contents: Classification and application models of recommender systems – Business goals – The consumer decision process – Virtual communities and network effects – Consumer-generated content (product reviews) – Functional aspects of recommender systems – Design aspects – Personalized and non-personalized recommendation methods – Research model and hypothesis – Influence of psychographic and sociodemographic determinants on the interest in personalized recommendations.
    Additional Edition: ISBN 9783631566220
    Language: English
    DOI: 10.3726/b13971
    URL: https://www.peterlang.com/view/product/60659?format=EPDF
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    Open Access Request
    HU Berlin
  • 4
    Online Resource
    Online Resource
    Product recommendations in e-commerce retailing applications / (2018)
    Frankfurt am Main, Germany :Peter Lang,
    Details
    UID:
    kobvindex_INT0003037
    Format: 1 electronic resource (xx, 197 pages) : , illustrations.
    Edition: Reprint.
    ISBN: 9783631566220 , 3631566220 , 9783631754528 , 3631754523
    Series Statement: Forschungsergebnisse der Wirtschaftsuniversität Wien ; 17
    Content: MACHINE-GENERATED SUMMARY NOTE: "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 quantitative research study investigates the influence of psychographic and sociodemographic determinants on the interest of consumers in personalized online book recommendations. The author presents new findings regarding the interest in recommendations, importance of product reviews for the decision process, motives for submitting ratings as well as comments, and the delivery of recommendations. The results show that opinion seeking, opinion leading, domain specific innovativeness, online shopping experience, and age are important factors in respect of the interest in personalized recommendations."
    Note: EDITORIAL NOTE: originally published: 2008. , DISSERTATION NOTE: originally submitted as doctoral thesis, Wirtschaftsuniversität Wien, 2006. , MACHINE-GENERATED CONTENTS NOTE: INTRODUCTION : Research goal ; Contents and organization -- RECOMMENDER SYSTEMS : DEFINITION, CLASSIFICATION, AND MARKETING PERSPECTIVES : Working definitions ; Classification ; Application models of recommender systems ; The consumer decision process ; Virtual communities -- RECOMMENDER SYSTEMS : FUNCTIONAL PERSPECTIVES : Input data of recommender systems ; Output data of recommender systems ; Measurement scales for preference elicitation ; Information delivery ; Recommendation methods -- RESEARCH MODEL, HYPOTHESES, AND METHODOLOGY : Problem statement ; Research questions and model ; Methodology and research design -- RESULTS : Descriptive results ; Verification of the research model -- SUMMARY AND DIRECTIONS FOR FURTHER RESEARCH : Main findings ; Limitations and directions for further research -- Bibliography.
    Language: English
    Keywords: Academic theses
    DOI: 10.3726/b13971
    URL: FULL
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    Berlin International
  • 5
    Online Resource
    Online Resource
    Product Recommendations in e-Commerce Retailing Applications. (2007)
    Frankfurt am Main :Peter Lang GmbH, Internationaler Verlag der Wissenschaften,
    Details
    UID:
    edocfu_9958982572502883
    Format: 1 online resource (222 pages)
    Edition: First edition.
    ISBN: 3-631-75452-3
    Series Statement: Forschungsergebnisse der Wirtschaftsuniversitaet Wien.
    Content: 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 quantitative research study investigates the influence of psychographic and sociodemographic determinants on the interest of consumers in personalized online book recommendations. The author presents new findings regarding the interest in recommendations, importance of product reviews for the decision process, motives for submitting ratings as well as comments, and the delivery of recommendations. The results show that opinion seeking, opinion leading, domain specific innovativeness, online shopping experience, and age are important factors in respect of the interest in personalized recommendations.
    Note: 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. , English.
    Additional Edition: ISBN 3-631-56622-0
    Language: English
    DOI: 10.3726/b13971
    Library Location Call Number Volume/Issue/Year Availability
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    Open Access Request
    FU Berlin
  • 6
    Online Resource
    Online Resource
    Product Recommendations in e-Commerce Retailing Applications. (2007)
    Frankfurt am Main :Peter Lang GmbH, Internationaler Verlag der Wissenschaften,
    Details
    UID:
    edoccha_9958982572502883
    Format: 1 online resource (222 pages)
    Edition: First edition.
    ISBN: 3-631-75452-3
    Series Statement: Forschungsergebnisse der Wirtschaftsuniversitaet Wien.
    Content: 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 quantitative research study investigates the influence of psychographic and sociodemographic determinants on the interest of consumers in personalized online book recommendations. The author presents new findings regarding the interest in recommendations, importance of product reviews for the decision process, motives for submitting ratings as well as comments, and the delivery of recommendations. The results show that opinion seeking, opinion leading, domain specific innovativeness, online shopping experience, and age are important factors in respect of the interest in personalized recommendations.
    Note: 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. , English.
    Additional Edition: ISBN 3-631-56622-0
    Language: English
    DOI: 10.3726/b13971
    Library Location Call Number Volume/Issue/Year Availability
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    Open Access Request
    Charité
  • 7
    Online Resource
    Online Resource
    Product Recommendations in e-Commerce Retailing Applications. (2007)
    Frankfurt am Main :Peter Lang GmbH, Internationaler Verlag der Wissenschaften,
    Details
    UID:
    almahu_9949561376802882
    Format: 1 online resource (222 pages)
    Edition: First edition.
    ISBN: 3-631-75452-3
    Series Statement: Forschungsergebnisse der Wirtschaftsuniversitaet Wien.
    Content: 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 quantitative research study investigates the influence of psychographic and sociodemographic determinants on the interest of consumers in personalized online book recommendations. The author presents new findings regarding the interest in recommendations, importance of product reviews for the decision process, motives for submitting ratings as well as comments, and the delivery of recommendations. The results show that opinion seeking, opinion leading, domain specific innovativeness, online shopping experience, and age are important factors in respect of the interest in personalized recommendations.
    Note: 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. , English.
    Additional Edition: ISBN 3-631-56622-0
    Language: English
    DOI: 10.3726/b13971
    Library Location Call Number Volume/Issue/Year Availability
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    Open Access Request
    HU Berlin
  • 8
    Online Resource
    Online Resource
    Product Recommendations in e-Commerce Retailing Applications. (2007)
    Frankfurt am Main :Peter Lang GmbH, Internationaler Verlag der Wissenschaften,
    Details
    UID:
    kobvindex_HPB1397632833
    Format: 1 online resource (222 pages).
    Edition: First edition.
    ISBN: 3631754523 , 9783631754528
    Series Statement: Forschungsergebnisse der Wirtschaftsuniversitaet Wien.
    Content: 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 quantitative research study investigates the influence of psychographic and sociodemographic determinants on the interest of consumers in personalized online book recommendations. The author presents new findings regarding the interest in recommendations, importance of product reviews for the decision process, motives for submitting ratings as well as comments, and the delivery of recommendations. The results show that opinion seeking, opinion leading, domain specific innovativeness, online shopping experience, and age are important factors in respect of the interest in personalized recommendations.
    Note: 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. , English.
    Additional Edition: ISBN 3-631-56622-0
    Language: English
    Keywords: Electronic books.
    DOI: 10.3726/b13971
    URL: Click here to view book
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    Open Access Request
    HPol Brandenburg
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