Recommender system with machine learning and artificial intelligence : practical tools and applications in medical, agricultural and other industries

목차 (Table of Contents)

• CONTENTS
• Preface = xix
• Acknowledgment = xxiii
• Part 1 : Introduction to Recommender Systems = 1
• 1 An Introduction to Basic Concepts on Recommender Systems / Pooja Rana ; Nishi Jain ; Usha Mittal = 3
• 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
• 1.5 Item-Based Recommendation vs. User-Based Recommendation System = 14
• 1.6 Evaluation Metrics for Recommendation Engines = 19
• 1.7 Problems with Recommendation Systems and Possible Solutions = 20
• 1.8 Applications of Recommender Systems = 24
• References = 25
• 2 A Brief Model Overview of Personalized Recommendation to Citizens in the Health-Care Industry / Subhasish Mohapatra ; Kunal Anand = 27
• 2.1 Introduction = 28
• 2.2 Methods Used in Recommender System = 29
• 2.3 Related Work = 33
• 2.4 Types of Explanation = 34
• 2.5 Explanation Methodology = 35
• 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 / Malik M. Saad Missen ; Mickaël Coustaty ; Hina Asmat ; Amnah Firdous ; Nadeem Akhtar ; Muhammad Akram ; V.B. Surya Prasath = 45
• 3.1 Introduction = 46
• 3.2 Information Exchange = 49
• 3.3 Information Extraction = 55
• 3.4 Sentiment Annotation = 57
• 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 / Sumanta Chandra Mishra Sharma ; Adway Mitra ; Deepayan Chakraborty = 73
• 4.1 Introduction = 73
• 4.2 Entities of Recommendation System = 74
• 4.3 Techniques of Recommendation = 76
• 4.4 Performance Evaluation = 82
• 4.5 Challenges = 83
• 4.6 Applications = 85
• 4.7 Conclusion = 85
• References = 85
• 5 A Machine Learning Approach to Recommend Suitable Crops and Fertilizers for Agriculture / Govind Kumar Jha ; Preetish Ranjan ; Manish Gaur = 89
• 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 / Abhaya Kumar Sahoo ; Chittaranjan Pradhan = 101
• 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 / G. Kanimozhi ; P. Shanmugavadivu ; M. Mary Shanthi Rani = 121
• 7.1 Introduction = 122
• 7.2 Related Works = 124
• 7.3 Methodology = 125
• 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 / Pooja Akulwar = 141
• 8.1 Introduction = 142
• 8.2 Machine Learning = 143
• 8.3 Recommender System = 151
• 8.4 Crop Management = 153
• 8.5 Application-Crop Disease Detection and Yield Prediction = 159
• References = 162
• Part 3 : Content-Based Recommender Systems = 165
• 9 Content-Based Recommender Systems / Poonam Bhatia Anand ; Rajender Nath = 167
• 9.1 Introduction = 167
• 9.2 Literature Review = 168
• 9.3 Recommendation Process = 172
• 9.4 Techniques Used for Item Representation and Learning User Profile = 176
• 9.5 Applicability of Recommender System in Healthcare and Agriculture = 182
• 9.6 Pros and Cons of Content-Based Recommender System = 186
• 9.7 Conclusion = 187
• References = 188
• 10 Content (Item)-Based Recommendation System / R. Balamurali = 197
• 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 / Soumya Prakash Rana ; Maitreyee Dey ; Javier Prieto ; Sandra Dudley = 215
• 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
• 11.6 Supervised Feature Selection & Weighting = 222
• 11.7 Feedback Collection = 225
• 11.8 Training & Health Recommendation Generation = 226
• 11.9 Evaluation of Content Based Health Recommender System = 228
• 11.10 Design Criteria of CBHRS = 229
• 11.11 Conclusions and Future Research Directions = 231
• References = 233
• 12 Context-Based Social Media Recommendation System / R. Sujithra Kanmani ; B. Surendiran = 237
• 12.1 Introduction = 237
• 12.2 Literature Survey = 240
• 12.3 Motivation and Objectives = 241
• 12.4 Performance Measures = 243
• 12.5 Precision = 243
• 12.6 Recall = 243
• 12.7 F- Measure = 244
• 12.8 Evaluation Results = 244
• 12.9 Conclusion and Future Work = 247
• References = 248
• 13 Netflix Challenge - Improving Movie Recommendations / Vasu Goel = 251
• 13.1 Introduction = 251
• 13.2 Data Preprocessing = 252
• 13.3 MovieLens Data = 253
• 13.4 Data Exploration = 255
• 13.5 Distributions = 256
• 13.6 Data Analysis = 257
• 13.7 Results = 265
• 13.8 Conclusion = 266
• References = 266
• 14 Product or Item-Based Recommender System / Jyoti Rani ; Usha Mittal ; Geetika Gupta = 269
• 14.1 Introduction = 270
• 14.2 Various Techniques to Design Food Recommendation System = 271
• 14.3 Implementation of Food Recommender System Using Content-Based Approach = 276
• 14.4 Results = 282
• 14.5 Observations = 283
• 14.6 Future Perspective of Recommender Systems = 283
• 14.7 Conclusion = 286
• Acknowledgements = 287
• References = 287
• Part 4 : Blockchain & IoT-Based Recommender Systems = 291
• 15 A Trust-Based Recommender System Built on IoT Blockchain Network With Cognitive Framework / S. Porkodi ; D. Kesavaraja = 293
• 15.1 Introduction = 294
• 15.2 Technologies and its Combinations = 297
• 15.3 Crypto Currencies With IoT-Case Studies = 299
• 15.4 Trust-Based Recommender System = 299
• 15.5 Recommender System Platform = 304
• 15.6 Conclusion and Future Directions = 307
• References = 307
• 16 Development of a Recommender System HealthMudra Using Blockchain for Prevention of Diabetes / Rashmi Bhardwaj ; Debabrata Datta = 313
• 16.1 Introduction = 314
• 16.2 Architecture of Blockchain = 317
• 16.3 Role of HealthMudra in Diabetic = 322
• 16.4 Blockchain Technology Solutions = 324
• 16.5 Conclusions = 325
• References = 326
• Part 5 : Healthcare Recommender Systems = 329
• 17 Case Study 1 : Health Care Recommender Systems / Usha Mittal ; Nancy Singla ; Geetika Gupta = 331
• 17.1 Introduction = 332
• 17.2 Review of Literature = 335
• 17.3 Recommender System for Parkinson's Disease (PD) = 341
• 17.4 Future Perspectives = 345
• 17.5 Conclusions = 346
• References = 348
• 18 Temporal Change Analysis-Based Recommender System for Alzheimer Disease Classification / S. Naganandhini ; P. Shanmugavadivu ; M. Mary Shanthi Rani = 351
• 18.1 Introduction = 352
• 18.2 Related Work = 352
• 18.3 Mechanism of TCA-RS-AD = 353
• 18.4 Experimental Dataset = 354
• 18.5 Neural Network = 357
• 18.6 Conclusion = 370
• References = 370
• 19 Regularization of Graphs : Sentiment Classification / R.S.M. Lakshmi Patibandla = 373
• 19.1 Introduction = 373
• 19.2 Neural Structured Learning = 374
• 19.3 Some Neural Network Models = 375
• 19.4 Experimental Results = 377
• 19.5 Conclusion = 383
• References = 384
• 20 TSARS : A Tree-Similarity Algorithm-Based Agricultural Recommender System / Madhusree Kuanr ; Puspanjali Mohapatra ; Sasmita Subhadarsinee Choudhury = 387
• 20.1 Introduction = 388
• 20.2 Literature Survey = 390
• 20.3 Research Gap = 393
• 20.4 Problem Definitions = 393
• 20.5 Methodology = 393
• 20.6 Results & Discussion = 394
• 20.7 Conclusion & Future Work = 397
• References = 399
• 21 Influenceable Targets Recommendation Analyzing Social Activities in Egocentric Online Social Networks / Soumyadeep Debnath ; Dhrubasish Sarkar ; Dipankar Das = 401
• 21.1 Introduction = 402
• 21.2 Literature Review = 403
• 21.3 Dataset Collection Process with Details = 404
• 21.4 Primary Preprocessing of Data = 406
• 21.5 Influence and Social Activities Analysis = 407
• 21.6 Recommendation System = 409
• 21.7 Top Most Influenceable Targets Evaluation = 413
• 21.8 Conclusion = 414
• 21.9 Future Scope = 415
• References = 415
• Index = 417