Recommender System with Machine Learning and Artificial Intelligence (eBook, PDF)

Practical Tools and Applications in Medical, Agricultural and Other Industries
Redaktion: Mohanty, Sachi Nandan; Gupta, Priya; Elngar, Ahmed A.; Jain, Sarika; Chatterjee, Jyotir Moy

• Format: PDF

Produktbeschreibung

This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 448
• Erscheinungstermin: 9. Juni 2020
• Englisch
• ISBN-13: 9781119711599
• Artikelnr.: 59605482

Autorenporträt

Sachi Nandan Mohanty received his PhD from IIT Kharagpur, India in 2015 and is now at ICFAI Foundation for Higher Education, Hyderabad, India. Jyotir Moy Chatterjee is working as an Assistant Professor (IT) at Lord Buddha Education Foundation, Kathmandu, Nepal. He has completed M.Tech in Computer Science & Engineering from Kalinga Institute of Industrial Technology, Bhubaneswar, India. Sarika Jain obtained her PhD in the field of Knowledge Representation in Artificial Intelligence in 2011. She has served in the field of education for over 18 years and is currently in service at the National Institute of Technology, Kurukshetra. Ahmed A. Elngar is the Founder and Head of Scientific Innovation Research Group (SIRG) and Assistant Professor of Computer Science at the Faculty of Computers and Information, Beni-Suef University, Egypt. Priya Gupta is working as an Assistant Professor in the Department of Computer Science at Maharaja Agrasen College, University of Delhi. Her Doctoral Degree is from BIT (Mesra), Ranchi.

Inhaltsangabe

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

3.2.2 Schema.org--The Future 51

3.2.2.1 Schema.org Extension Mechanism 52

3.2.2.2 Schema.org Tourism Vocabulary 52

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