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Produktbild: Recommender System

Recommender System Practical Tools and Applications in Medical, Agricultural and Other Industries

264,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.01.1900

Herausgeber

Sachi Nandan Mohanty + weitere

Verlag

John Wiley & Sons Inc

Seitenzahl

448

Maße (L/B/H)

22,9/15,2/2,5 cm

Gewicht

767 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-71157-5

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

01.01.1900

Herausgeber

Verlag

John Wiley & Sons Inc

Seitenzahl

448

Maße (L/B/H)

22,9/15,2/2,5 cm

Gewicht

767 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-119-71157-5

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Recommender System
  • Preface xix

    Acknowledgment xxiii

    Part 1: Introduction to Recommender Systems 1

    1 An Introduction to Basic Concepts on Recommender Systems 3
    Pooja Rana, Nishi Jain and Usha Mittal

    1.1 Introduction 4

    1.2 Functions of Recommendation Systems 5

    1.3 Data and Knowledge Sources 6

    1.4 Types of Recommendation Systems 8

    1.4.1 Content-Based 8

    1.4.1.1 Advantages of Content-Based Recommendation 11

    1.4.1.2 Disadvantages of Content-Based Recommendation 11

    1.4.2 Collaborative Filtering 12

    1.5 Item-Based Recommendation vs. User-Based Recommendation System 14

    1.5.1 Advantages of Memory-Based Collaborative Filtering 15

    1.5.2 Shortcomings 16

    1.5.3 Advantages of Model-Based Collaborative Filtering 17

    1.5.4 Shortcomings 17

    1.5.5 Hybrid Recommendation System 17

    1.5.6 Advantages of Hybrid Recommendation Systems 18

    1.5.7 Shortcomings 18

    1.5.8 Other Recommendation Systems 18

    1.6 Evaluation Metrics for Recommendation Engines 19

    1.7 Problems with Recommendation Systems and Possible Solutions 20

    1.7.1 Advantages of Recommendation Systems 23

    1.7.2 Disadvantages of Recommendation Systems 24

    1.8 Applications of Recommender Systems 24

    References 25

    2 A Brief Model Overview of Personalized Recommendation to Citizens in the Health-Care Industry 27
    Subhasish Mohapatra and Kunal Anand

    2.1 Introduction 28

    2.2 Methods Used in Recommender System 29

    2.2.1 Content-Based 29

    2.2.2 Collaborative Filtering 32

    2.2.3 Hybrid Filtering 33

    2.3 Related Work 33

    2.4 Types of Explanation 34

    2.5 Explanation Methodology 35

    2.5.1 Collaborative-Based 36

    2.5.2 Content-Based 36

    2.5.3 Knowledge and Utility-Based 37

    2.5.4 Case-Based 37

    2.5.5 Demographic-Based 38

    2.6 Proposed Theoretical Framework for Explanation-Based Recommender System in Health-Care Domain 39

    2.7 Flowchart 39

    2.8 Conclusion 41

    References 41

    3 2Es of TIS: A Review of Information Exchange and Extraction in Tourism Information Systems 45
    Malik M. Saad Missen, Mickaël Coustaty, Hina Asmat, Amnah Firdous, Nadeem Akhtar, Muhammad Akram and V. B. Surya Prasath

    3.1 Introduction 46

    3.2 Information Exchange 49

    3.2.1 Exchange of Tourism Objects Data 49

    3.2.1.1 Semantic Clashes 50

    3.2.1.2 Structural Clashes 50

    3.2.3 Exchange of Tourism-Related Statistical Data 53

    3.3 Information Extraction 55

    3.3.1 Opinion Extraction 56

    3.3.2 Opinion Mining 57

    3.4 Sentiment Annotation 57

    3.4.1 SentiML 58

    3.4.1.1 SentiML Example 58

    3.4.2 OpinionMiningML 59

    3.4.2.1 OpinionMiningML Example 60

    3.4.3 EmotionML 61

    3.4.3.1 EmotionML Example 61

    3.5 Comparison of Different Annotations Schemes 62

    3.6 Temporal and Event Extraction 64

    3.7 TimeML 65

    3.8 Conclusions 67

    References 67

    Part 2: Machine Learning-Based Recommender Systems 71

    4 Concepts of Recommendation System from the Perspective of Machine Learning 73
    Sumanta Chandra Mishra Sharma, Adway Mitra and Deepayan Chakraborty

    4.1 Introduction 73

    4.2 Entities of Recommendation System 74

    4.2.1 User 74

    4.2.2 Items 75