Recommender System with Machine Learning and Artificial Intelligence : Practical Tools and Applications in Medical, Agricultural and Other Industries

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Author(s):	Elngar, Ahmed Mohanty, Sachi Nandan
ISBN No.:	9781119711575
Pages:	448
Year:	202007
Format:	Trade Cloth (Hard Cover)
Price:	$ 326.99
Dispatch delay:	Dispatched between 7 to 15 days
Status:	Available

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Table of contents
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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 4.2.3 Action 75 4.3 Techniques of Recommendation 76 4.3.1 Personalized Recommendation System 77 4.3.2 Non-Personalized Recommendation System 77 4.

3.3 Content-Based Filtering 77 4.3.4 Collaborative Filtering 78 4.3.5 Model-Based Filtering 80 4.3.6 Memory-Based Filtering 80 4.

3.7 Hybrid Recommendation Technique 81 4.3.8 Social Media Recommendation Technique 82 4.4 Performance Evaluation 82 4.5 Challenges 83 4.5.1 Sparsity of Data 84 4.

5.2 Scalability 84 4.5.3 Slow Start 84 4.5.4 Gray Sheep and Black Sheep 84 4.5.5 Item Duplication 84 4.

5.6 Privacy Issue 84 4.5.7 Biasness 85 4.6 Applications 85 4.7 Conclusion 85 References 85 5 A Machine Learning Approach to Recommend Suitable Crops and Fertilizers for Agriculture 89 Govind Kumar Jha, Preetish Ranjan and Manish Gaur 5.1 Introduction 90 5.2 Literature Review 91 5.

3 Methodology 93 5.4 Results and Analysis 96 5.5 Conclusion 97 References 98 6 Accuracy-Assured Privacy-Preserving Recommender System Using Hybrid-Based Deep Learning Method 101 Abhaya Kumar Sahoo and Chittaranjan Pradhan 6.1 Introduction 102 6.2 Overview of Recommender System 103 6.3 Collaborative Filtering-Based Recommender System 106 6.4 Machine Learning Methods Used in Recommender System 107 6.5 Proposed RBM Model-Based Movie Recommender System 110 6.

6 Proposed CRBM Model-Based Movie Recommender System 113 6.7 Conclusion and Future Work 115 References 118 7 Machine Learning-Based Recommender System for Breast Cancer Prognosis 121 G. Kanimozhi, P. Shanmugavadivu and M. Mary Shanthi Rani 7.1 Introduction 122 7.2 Related Works 124 7.3 Methodology 125 7.

3.1 Experimental Dataset 125 7.3.2 Feature Selection 127 7.3.3 Functional Phases of MLRS-BC 128 7.3.4 Prediction Algorithms 129 7.

4 Results and Discussion 131 7.5 Conclusion 138 Acknowledgment 139 References 139 8 A Recommended System for Crop Disease Detection and Yield Prediction Using Machine Learning Approach 141 Pooja Akulwar 8.1 Introduction 142 8.2 Machine Learning 143 8.2.1 Overview 143 8.2.2 Machine Learning Algorithms 145 8.

2.3 Machine Learning Methods 146 8.2.3.1 Artificial Neural Network 146 8.2.3.2 Support Vector Machines 146 8.

2.3.3 K-Nearest Neighbors (K-NN) 147 8.2.3.4 Decision Tree Learning 147 8.2.3.

5 Random Forest 148 8.2.3.6 Gradient Boosted Decision Tree (GBDT) 149 8.2.3.7 Regularized Greedy Forest (RGF) 150 8.3 Recommender System 151 8.

3.1 Overview 151 8.4 Crop Management 153 8.4.1 Yield Prediction 153 8.4.2 Disease Detection 154 8.4.

3 Weed Detection 156 8.4.4 Crop Quality 159 8.5 Application--Crop Disease Detection and Yield Prediction 159 References 162 Part 3: Content-Based Recommender Systems 165 9 Content-Based Recommender Systems 167 Poonam Bhatia Anand and Rajender Nath 9.1 Introduction 167 9.2 Literature Review 168 9.3 Recommendation Process 172 9.3.

1 Architecture of Content-Based Recommender System 172 9.3.2 Profile Cleaner Representation 175 9.4 Techniques Used for Item Representation and Learning User Profile 176 9.4.1 Representation of Content 176 9.4.2 Vector Space Model Based on Keywords 177 9.

4.3 Techniques for Learning Profiles of User 179 9.4.3.1 Probabilistic Method 179 9.4.3.2 Rocchio''s and Relevance Feedback Method 180 9.

4.3.3 Other Methods 181 9.5 Applicability of Recommender System in Healthcare and Agriculture 182 9.5.1 Recommendation System in Healthcare 182 9.5.2 Recommender System in Agriculture 184 9.

6 Pros and Cons of Content-Based Recommender System 186 9.7 Conclusion 187 References 188 10 Content (Item)-Based Recommendation System 197 R. Balamurali 10.1 Introduction 198 10.2 Phases of Content-Based Recommendation Generation 198 10.3 Content-Based Recommendation Using Cosine Similarity 199 10.4 Content-Based Recommendations Using Optimization Techniques 204 10.5 Content-Based Recommendation Using the Tree Induction Algorithm 208 10.

6 Summary 212 References 213 11 Content-Based Health Recommender Systems 215 Soumya Prakash Rana, Maitreyee Dey, Javier Prieto and Sandra Dudley 11.1 Introduction 216 11.2 Typical Health Recommender System Framework 217 11.3 Components of Content-Based Health Recommender System 218 11.4 Unstructured Data Processing 220 11.5 Unsupervised Feature Extraction & Weighting 221 11.5.1 Bag of Words (BoW) 221 11.

5.2 Word to Vector (Word2Vec) 222 11.5.3 Global Vectors for Word Representations (Glove) 222 11.6 Supervised Feature Selection & Weighting 222 11.7 Feedback Collection 225 11.7.1 Medication & Therapy 225 11.

7.2 Healthy Diet Plan 225 11.7.3 Suggestions 225 11.8 Training & Health Recommendation Generation 226 11.8.1 Analogy-Based ML in CBHRS 227 11.8.

2 Specimen-Based ML in CBHRS 227 11.9 Evaluation of Content Based Health Recommender System 228 11.10 Design Criteria of CBHRS 229 11.10.1 Micro-Level & Lucidity 230 11.10.2 Interactive Interface 230 11.10.

3 Data Protection 230 11.10.4 Risk & Uncertainty Management 231 11.10.5 Doctor-in-Loop (DiL) 231 11.11 Conclusions and Future Research Directions 231 References 233 12 Context-Based Social Media Recommendation System 237 R. Sujithra Kanmani and B. Surendiran 12.

1 Introduction 237 12.2 Literature Survey 240 12.3 Motivation and Objectives 241 12.3.1 Architecture 241 12.3.2 Modules 242 12.3.

3 Implementation Details 243 12.4 Performance Measures 243 12.5 Precision 243 12.6 Recall 243 12.7 F-.

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