book

Recommender System with Machine Learning and Artificial Intelligence

Name: Recommender System with Machine Learning and Artificial Intelligence
ISBN: 9781119711575

by Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Sarika Jain, Ahmed A. Elngar, Priya Gupta

July 2020

Intermediate to advanced

448 pages

11h 7m

English

Wiley-Scrivener

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Cover
Preface
Acknowledgment
Part I: INTRODUCTION TO RECOMMENDER SYSTEMS
1 An Introduction to Basic Concepts on Recommender Systems
1.1 Introduction1.2 Functions of Recommendation Systems1.3 Data and Knowledge Sources1.4 Types of Recommendation Systems1.5 Item-Based Recommendation vs. User-Based Recommendation System1.6 Evaluation Metrics for Recommendation Engines1.7 Problems with Recommendation Systems and Possible Solutions1.8 Applications of Recommender SystemsReferences
2 A Brief Model Overview of Personalized Recommendation to Citizens in the Health-Care Industry
2.1 Introduction2.2 Methods Used in Recommender System2.3 Related Work2.4 Types of Explanation2.5 Explanation Methodology2.6 Proposed Theoretical Framework for Explanation-Based Recommender System in Health-Care Domain2.7 Flowchart2.8 ConclusionReferences
3 2Es of TIS: A Review of Information Exchange and Extraction in Tourism Information Systems
3.1 Introduction3.2 Information Exchange3.3 Information Extraction3.4 Sentiment Annotation3.5 Comparison of Different Annotations Schemes3.6 Temporal and Event Extraction3.7 TimeML3.8 ConclusionsReferences
Part 2: MACHINE LEARNING-BASED RECOMMENDER SYSTEMS
4 Concepts of Recommendation System from the Perspective of Machine Learning
4.1 Introduction4.2 Entities of Recommendation System4.3 Techniques of Recommendation4.4 Performance Evaluation4.5 Challenges4.6 Applications4.7 ConclusionReferences
5 A Machine Learning Approach to Recommend Suitable Crops and Fertilizers for Agriculture
5.1 Introduction5.2 Literature Review5.3 Methodology5.4 Results and Analysis5.5 ConclusionReferences

6 Accuracy-Assured Privacy-Preserving Recommender System Using Hybrid-Based Deep Learning Method
6.1 Introduction6.2 Overview of Recommender System6.3 Collaborative Filtering-Based Recommender System6.4 Machine Learning Methods Used in Recommender System6.5 Proposed RBM Model-Based Movie Recommender System6.6 Proposed CRBM Model-Based Movie Recommender System6.7 Conclusion and Future WorkReferences
7 Machine Learning-Based Recommender System for Breast Cancer Prognosis
7.1 Introduction7.2 Related Works7.3 Methodology7.4 Results and Discussion7.5 ConclusionAcknowledgmentReferences
8 A Recommended System for Crop Disease Detection and Yield Prediction Using Machine Learning Approach
8.1 Introduction8.2 Machine Learning8.3 Recommender System8.4 Crop Management8.5 Application—Crop Disease Detection and Yield PredictionReferences
Part 3: CONTENT-BASED RECOMMENDER SYSTEMS
9 Content-Based Recommender Systems
9.1 Introduction9.2 Literature Review9.3 Recommendation Process9.4 Techniques Used for Item Representation and Learning User Profile9.5 Applicability of Recommender System in Healthcare and Agriculture9.6 Pros and Cons of Content-Based Recommender System9.7 ConclusionReferences
10 Content (Item)-Based Recommendation System
10.1 Introduction10.2 Phases of Content-Based Recommendation Generation10.3 Content-Based Recommendation Using Cosine Similarity10.4 Content-Based Recommendations Using Optimization Techniques10.5 Content-Based Recommendation Using the Tree Induction Algorithm10.6 SummaryReferences
11 Content-Based Health Recommender Systems
11.1 Introduction11.2 Typical Health Recommender System Framework11.3 Components of Content-Based Health Recommender System11.4 Unstructured Data Processing11.5 Unsupervised Feature Extraction & Weighting11.6 Supervised Feature Selection & Weighting11.7 Feedback Collection11.8 Training & Health Recommendation Generation11.9 Evaluation of Content-Based Health Recommender System11.10 Design Criteria of CBHRS11.11 Conclusions and Future Research DirectionsReferences
12 Context-Based Social Media Recommendation System
12.1 Introduction12.2 Literature Survey12.3 Motivation and Objectives12.4 Performance Measures12.5 Precision12.6 Recall12.7 F- Measure12.8 Evaluation Results12.9 Conclusion and Future WorkReferences
13 Netflix Challenge—Improving Movie Recommendations
13.1 Introduction13.2 Data Preprocessing13.3 MovieLens Data13.4 Data Exploration13.5 Distributions13.6 Data Analysis13.7 Results13.8 ConclusionReferences
14 Product or Item-Based Recommender System
14.1 Introduction14.2 Various Techniques to Design Food Recommendation System14.3 Implementation of Food Recommender System Using Content-Based Approach14.4 Results14.5 Observations14.6 Future Perspective of Recommender Systems14.7 ConclusionAcknowledgementsReferences
Part 4: BLOCKCHAIN & IOT-BASED RECOMMENDER SYSTEMS
15 A Trust-Based Recommender System Built on IoT Blockchain Network With Cognitive Framework
15.1 Introduction15.2 Technologies and its Combinations15.3 Crypto Currencies With IoT–Case Studies15.4 Trust-Based Recommender System15.5 Recommender System Platform15.6 Conclusion and Future DirectionsReferences
16 Development of a Recommender System HealthMudra Using Blockchain for Prevention of Diabetes
16.1 Introduction16.2 Architecture of Blockchain16.3 Role of HealthMudra in Diabetic16.4 Blockchain Technology Solutions16.5 ConclusionsReferences
Part 5: HEALTHCARE RECOMMENDER SYSTEMS
17 Case Study 1: Health Care Recommender Systems
17.1 Introduction17.2 Review of Literature17.3 Recommender System for Parkinson’s Disease (PD)17.4 Future Perspectives17.5 ConclusionsReferences
18 Temporal Change Analysis-Based Recommender System for Alzheimer Disease Classification
18.1 Introduction18.2 Related Work18.3 Mechanism of TCA-RS-AD18.4 Experimental Dataset18.5 Neural Network18.6 ConclusionReferences
19 Regularization of Graphs: Sentiment Classification
19.1 Introduction19.2 Neural Structured Learning19.3 Some Neural Network Models19.4 Experimental Results19.5 ConclusionReferences
20 TSARS: A Tree-Similarity Algorithm-Based Agricultural Recommender System
20.1 Introduction20.2 Literature Survey20.3 Research Gap20.4 Problem Definitions20.5 Methodology20.6 Results & Discussion20.7 Conclusion & Future WorkReferences
21 Influenceable Targets Recommendation Analyzing Social Activities in Egocentric Online Social Networks
21.1 Introduction21.2 Literature Review21.3 Dataset Collection Process with Details21.4 Primary Preprocessing of Data21.5 Influence and Social Activities Analysis21.6 Recommendation System21.7 Top Most Influenceable Targets Evaluation21.8 Conclusion21.9 Future ScopeReferences
Index
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Acknowledgment

I would like to acknowledge the most important people in my life, my father Aloke Moy Chatterjee, my uncle Mr. Moni Moy Chatterjee & my late mother Nomita Chatterjee. This book has been my long-cherished dream which would not have been turned into reality without the support and love of these amazing people. They have continuously encouraged me despite my failing to give them the proper time and attention. I am also grateful to my friends, who have encouraged and blessed this work with their unconditional love and patience.

Jyotir Moy ChatterjeeDepartment of ITLord Buddha Education Foundation (APUTI)KathmanduNepal-44600

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Recommender System with Machine Learning and Artificial Intelligence

by Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Sarika Jain, Ahmed A. Elngar, Priya Gupta

Acknowledgment

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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