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Recommender System with Machine Learning and Artificial Intelligence

Practical Tools and Applications in Medical, Agricultural and Other Industries
1. Aufl.

von: Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Sarika Jain, Ahmed A. Elngar, Priya Gupta
197,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	16.07.2020
ISBN/EAN:	9781119711605
Sprache:	englisch
Anzahl Seiten:	448

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Beschreibungen

Titelbeschreibung

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.

Inhaltsverzeichnis

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- Measure 244 12.8 Evaluation Results 244 12.9 Conclusion and Future Work 247 References 248 13 Netflix Challenge—Improving Movie Recommendations 251 Vasu Goel 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 269 Jyoti Rani, Usha Mittal and Geetika Gupta 14.1 Introduction 270 14.2 Various Techniques to Design Food Recommendation System 271 14.2.1 Collaborative Filtering Recommender Systems 271 14.2.2 Content-Based Recommender Systems (CB) 272 14.2.3 Knowledge-Based Recommender Systems 272 14.2.4 Hybrid Recommender Systems 273 14.2.5 Context Aware Approaches 273 14.2.6 Group-Based Methods 273 14.2.7 Different Types of Food Recommender Systems 273 14.3 Implementation of Food Recommender System Using Content-Based Approach 276 14.3.1 Item Profile Representation 277 14.3.2 Information Retrieval 278 14.3.3 Word2vec 278 14.3.4 How are word2vec Embedding’s Obtained? 278 14.3.5 Obtaining word2vec Embeddings 279 14.3.6 Dataset 280 14.3.6.1 Data Preprocessing 280 14.3.7 Web Scrapping For Food List 280 14.3.7.1 Porter Stemming All Words 280 14.3.7.2 Filtering Our Ingredients 280 14.3.7.3 Final Data Frame with Dishes and Their Ingredients 281 14.3.7.4 Hamming Distance 281 14.3.7.5 Jaccard Distance 282 14.4 Results 282 14.5 Observations 283 14.6 Future Perspective of Recommender Systems 283 14.6.1 User Information Challenges 283 14.6.1.1 User Nutrition Information Uncertainty 283 14.6.1.2 User Rating Data Collection 284 14.6.2 Recommendation Algorithms Challenges 284 14.6.2.1 User Information Such as Likes/ Dislikes Food or Nutritional Needs 284 14.6.2.2 Recipe Databases 284 14.6.2.3 A Set of Constraints or Rules 285 14.6.3 Challenges Concerning Changing Eating Behavior of Consumers 285 14.6.4 Challenges Regarding Explanations and Visualizations 286 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 293 S. Porkodi and D. Kesavaraja 15.1 Introduction 294 15.1.1 Today and Tomorrow 294 15.1.2 Vision 294 15.1.3 Internet of Things 294 15.1.4 Blockchain 295 15.1.5 Cognitive Systems 296 15.1.6 Application 296 15.2 Technologies and its Combinations 297 15.2.1 IoT–Blockchain 297 15.2.2 IoT–Cognitive System 298 15.2.3 Blockchain–Cognitive System 298 15.2.4 IoT–Blockchain–Cognitive System 298 15.3 Crypto Currencies With IoT–Case Studies 299 15.4 Trust-Based Recommender System 299 15.4.1 Requirement 299 15.4.2 Things Management 302 15.4.3 Cognitive Process 303 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 313 Rashmi Bhardwaj and Debabrata Datta 16.1 Introduction 314 16.2 Architecture of Blockchain 317 16.2.1 Definition of Blockchain 318 16.2.2 Structure of Blockchain 318 16.3 Role of HealthMudra in Diabetic 322 16.4 Blockchain Technology Solutions 324 16.4.1 Predictive Models of Health Data Analysis 325 16.5 Conclusions 325 References 326 Part 5: Healthcare Recommender Systems 329 17 Case Study 1: Health Care Recommender Systems 331 Usha Mittal, Nancy Singla and Geetika Gupta 17.1 Introduction 332 17.1.1 Health Care Recommender System 332 17.1.2 Parkinson’s Disease: Causes and Symptoms 333 17.1.3 Parkinson’s Disease: Treatment and Surgical Approaches 334 17.2 Review of Literature 335 17.2.1 Machine Learning Algorithms for Parkinson’s Data 337 17.2.2 Visualization 340 17.3 Recommender System for Parkinson’s Disease (PD) 341 17.3.1 How Will One Know When Parkinson’s has Progressed? 342 17.3.2 Dataset for Parkinson’s Disease (PD) 342 17.3.3 Feature Selection 343 17.3.4 Classification 343 17.3.4.1 Logistic Regression 343 17.3.4.2 K Nearest Neighbor (KNN) 343 17.3.4.3 Support Vector Machine (SVM) 344 17.3.4.4 Decision Tree 344 17.3.5 Train and Test Data 344 17.3.6 Recommender System 344 17.4 Future Perspectives 345 17.5 Conclusions 346 References 348 18 Temporal Change Analysis-Based Recommender System for Alzheimer Disease Classification 351 S. Naganandhini, P. Shanmugavadivu and M. Mary Shanthi Rani 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 373 R.S.M. Lakshmi Patibandla 19.1 Introduction 373 19.2 Neural Structured Learning 374 19.3 Some Neural Network Models 375 19.4 Experimental Results 377 19.4.1 Base Model 379 19.4.2 Graph Regularization 382 19.5 Conclusion 383 References 384 20 TSARS: A Tree-Similarity Algorithm-Based Agricultural Recommender System 387 Madhusree Kuanr, Puspanjali Mohapatra and Sasmita Subhadarsinee Choudhury 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 20.6.1 Performance Evaluation 394 20.6.2 Time Complexity Analysis 396 20.7 Conclusion & Future Work 397 References 399 21 Influenceable Targets Recommendation Analyzing Social Activities in Egocentric Online Social Networks 401 Soumyadeep Debnath, Dhrubasish Sarkar and Dipankar Das 21.1 Introduction 402 21.2 Literature Review 403 21.3 Dataset Collection Process with Details 404 21.3.1 Main User’s Activities Data 405 21.3.2 Network Member’s Activities Data 405 21.3.3 Tools and Libraries for Data Collection 405 21.3.4 Details of the Datasets 406 21.4 Primary Preprocessing of Data 406 21.4.1 Language Detection and Translation 406 21.4.2 Tagged Tweeters Collection 407 21.4.3 Textual Noise Removal 407 21.4.4 Textual Spelling and Correction 407 21.5 Influence and Social Activities Analysis 407 21.5.1 Step 1: Targets Selection From OSMs 408 21.5.2 Step 3: Categories Classification of Social Contents 408 21.5.3 Step 4: Sentiments Analysis of Social Contents 408 21.6 Recommendation System 409 21.6.1 Secondary Preprocessing of Data 409 21.6.2 Recommendation Analyzing Contents of Social Activities 411 21.7 Top Most Influenceable Targets Evaluation 413 21.8 Conclusion 414 21.9 Future Scope 415 References 415 Index 417

Autorenportrait

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.

Back cover copy

This book comprehensively covers all the topics of cutting-edge and emerging recommender systems that provide personalized recommendations of items or services based on past behavior. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. The 21 chapters are placed in 5 broad sections looking at Introduction to Recommender Systems; Machine Learning-Based Recommender Systems; Content-Based Recommender Systems; Blockchain & IoT-Based Recommender Systems; and Healthcare Recommender Systems. Recommendations in specific 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. Various robustness aspects of recommender systems, such as trust-based recommendation system, recommendation system on tourist, medical sciences, and the agricultural field are discussed. In addition, current topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria decision support systems, and active learning systems, are introduced together with applications. Audience The book will be used by engineers in information technology, artificial intelligence, human-computer interaction, machine learning and analytics specialists, as well as marketeers and web managers in industry.