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.

• Preface xixAcknowledgment xxiiiPart 1: Introduction to Recommender Systems 11 An Introduction to Basic Concepts on Recommender Systems 3Pooja Rana, Nishi Jain and Usha Mittal1.1 Introduction 41.2 Functions of Recommendation Systems 51.3 Data and Knowledge Sources 61.4 Types of Recommendation Systems 81.4.1 Content-Based 81.4.1.1 Advantages of Content-Based Recommendation 111.4.1.2 Disadvantages of Content-Based Recommendation 111.4.2 Collaborative Filtering 121.5 Item-Based Recommendation vs. User-Based Recommendation System 141.5.1 Advantages of Memory-Based Collaborative Filtering 151.5.2 Shortcomings 161.5.3 Advantages of Model-Based Collaborative Filtering 171.5.4 Shortcomings 171.5.5 Hybrid Recommendation System 171.5.6 Advantages of Hybrid Recommendation Systems 181.5.7 Shortcomings 181.5.8 Other Recommendation Systems 181.6 Evaluation Metrics for Recommendation Engines 191.7 Problems with Recommendation Systems and Possible Solutions 201.7.1 Advantages of Recommendation Systems 231.7.2 Disadvantages of Recommendation Systems 241.8 Applications of Recommender Systems 24References 252 A Brief Model Overview of Personalized Recommendation to Citizens in the Health-Care Industry 27Subhasish Mohapatra and Kunal Anand2.1 Introduction 282.2 Methods Used in Recommender System 292.2.1 Content-Based 292.2.2 Collaborative Filtering 322.2.3 Hybrid Filtering 332.3 Related Work 332.4 Types of Explanation 342.5 Explanation Methodology 352.5.1 Collaborative-Based 362.5.2 Content-Based 362.5.3 Knowledge and Utility-Based 372.5.4 Case-Based 372.5.5 Demographic-Based 382.6 Proposed Theoretical Framework for Explanation-Based Recommender System in Health-Care Domain 392.7 Flowchart 392.8 Conclusion 41References 413 2Es of TIS: A Review of Information Exchange and Extraction in Tourism Information Systems 45Malik M. Saad Missen, Mickaël Coustaty, Hina Asmat, Amnah Firdous, Nadeem Akhtar, Muhammad Akram and V. B. Surya Prasath3.1 Introduction 463.2 Information Exchange 493.2.1 Exchange of Tourism Objects Data 493.2.1.1 Semantic Clashes 503.2.1.2 Structural Clashes 503.2.2 Schema.org—The Future 513.2.2.1 Schema.org Extension Mechanism 523.2.2.2 Schema.org Tourism Vocabulary 523.2.3 Exchange of Tourism-Related Statistical Data 533.3 Information Extraction 553.3.1 Opinion Extraction 563.3.2 Opinion Mining 573.4 Sentiment Annotation 573.4.1 SentiML 583.4.1.1 SentiML Example 583.4.2 OpinionMiningML 593.4.2.1 OpinionMiningML Example 603.4.3 EmotionML 613.4.3.1 EmotionML Example 613.5 Comparison of Different Annotations Schemes 623.6 Temporal and Event Extraction 643.7 TimeML 653.8 Conclusions 67References 67Part 2: Machine Learning-Based Recommender Systems 714 Concepts of Recommendation System from the Perspective of Machine Learning 73Sumanta Chandra Mishra Sharma, Adway Mitra and Deepayan Chakraborty4.1 Introduction 734.2 Entities of Recommendation System 744.2.1 User 744.2.2 Items 754.2.3 Action 754.3 Techniques of Recommendation 764.3.1 Personalized Recommendation System 774.3.2 Non-Personalized Recommendation System 774.3.3 Content-Based Filtering 774.3.4 Collaborative Filtering 784.3.5 Model-Based Filtering 804.3.6 Memory-Based Filtering 804.3.7 Hybrid Recommendation Technique 814.3.8 Social Media Recommendation Technique 824.4 Performance Evaluation 824.5 Challenges 834.5.1 Sparsity of Data 844.5.2 Scalability 844.5.3 Slow Start 844.5.4 Gray Sheep and Black Sheep 844.5.5 Item Duplication 844.5.6 Privacy Issue 844.5.7 Biasness 854.6 Applications 854.7 Conclusion 85References 855 A Machine Learning Approach to Recommend Suitable Crops and Fertilizers for Agriculture 89Govind Kumar Jha, Preetish Ranjan and Manish Gaur5.1 Introduction 905.2 Literature Review 915.3 Methodology 935.4 Results and Analysis 965.5 Conclusion 97References 986 Accuracy-Assured Privacy-Preserving Recommender System Using Hybrid-Based Deep Learning Method 101Abhaya Kumar Sahoo and Chittaranjan Pradhan6.1 Introduction 1026.2 Overview of Recommender System 1036.3 Collaborative Filtering-Based Recommender System 1066.4 Machine Learning Methods Used in Recommender System 1076.5 Proposed RBM Model-Based Movie Recommender System 1106.6 Proposed CRBM Model-Based Movie Recommender System 1136.7 Conclusion and Future Work 115References 1187 Machine Learning-Based Recommender System for Breast Cancer Prognosis 121G. Kanimozhi, P. Shanmugavadivu and M. Mary Shanthi Rani7.1 Introduction 1227.2 Related Works 1247.3 Methodology 1257.3.1 Experimental Dataset 1257.3.2 Feature Selection 1277.3.3 Functional Phases of MLRS-BC 1287.3.4 Prediction Algorithms 1297.4 Results and Discussion 1317.5 Conclusion 138Acknowledgment 139References 1398 A Recommended System for Crop Disease Detection and Yield Prediction Using Machine Learning Approach 141Pooja Akulwar8.1 Introduction 1428.2 Machine Learning 1438.2.1 Overview 1438.2.2 Machine Learning Algorithms 1458.2.3 Machine Learning Methods 1468.2.3.1 Artificial Neural Network 1468.2.3.2 Support Vector Machines 1468.2.3.3 K-Nearest Neighbors (K-NN) 1478.2.3.4 Decision Tree Learning 1478.2.3.5 Random Forest 1488.2.3.6 Gradient Boosted Decision Tree (GBDT) 1498.2.3.7 Regularized Greedy Forest (RGF) 1508.3 Recommender System 1518.3.1 Overview 1518.4 Crop Management 1538.4.1 Yield Prediction 1538.4.2 Disease Detection 1548.4.3 Weed Detection 1568.4.4 Crop Quality 1598.5 Application—Crop Disease Detection and Yield Prediction 159References 162Part 3: Content-Based Recommender Systems 1659 Content-Based Recommender Systems 167Poonam Bhatia Anand and Rajender Nath9.1 Introduction 1679.2 Literature Review 1689.3 Recommendation Process 1729.3.1 Architecture of Content-Based Recommender System 1729.3.2 Profile Cleaner Representation 1759.4 Techniques Used for Item Representation and Learning User Profile 1769.4.1 Representation of Content 1769.4.2 Vector Space Model Based on Keywords 1779.4.3 Techniques for Learning Profiles of User 1799.4.3.1 Probabilistic Method 1799.4.3.2 Rocchio’s and Relevance Feedback Method 1809.4.3.3 Other Methods 1819.5 Applicability of Recommender System in Healthcare and Agriculture 1829.5.1 Recommendation System in Healthcare 1829.5.2 Recommender System in Agriculture 1849.6 Pros and Cons of Content-Based Recommender System 1869.7 Conclusion 187References 18810 Content (Item)-Based Recommendation System 197R. Balamurali10.1 Introduction 19810.2 Phases of Content-Based Recommendation Generation 19810.3 Content-Based Recommendation Using Cosine Similarity 19910.4 Content-Based Recommendations Using Optimization Techniques 20410.5 Content-Based Recommendation Using the Tree Induction Algorithm 20810.6 Summary 212References 21311 Content-Based Health Recommender Systems 215Soumya Prakash Rana, Maitreyee Dey, Javier Prieto and Sandra Dudley11.1 Introduction 21611.2 Typical Health Recommender System Framework 21711.3 Components of Content-Based Health Recommender System 21811.4 Unstructured Data Processing 22011.5 Unsupervised Feature Extraction & Weighting 22111.5.1 Bag of Words (BoW) 22111.5.2 Word to Vector (Word2Vec) 22211.5.3 Global Vectors for Word Representations (Glove) 22211.6 Supervised Feature Selection & Weighting 22211.7 Feedback Collection 22511.7.1 Medication & Therapy 22511.7.2 Healthy Diet Plan 22511.7.3 Suggestions 22511.8 Training & Health Recommendation Generation 22611.8.1 Analogy-Based ML in CBHRS 22711.8.2 Specimen-Based ML in CBHRS 22711.9 Evaluation of Content Based Health Recommender System 22811.10 Design Criteria of CBHRS 22911.10.1 Micro-Level & Lucidity 23011.10.2 Interactive Interface 23011.10.3 Data Protection 23011.10.4 Risk & Uncertainty Management 23111.10.5 Doctor-in-Loop (DiL) 23111.11 Conclusions and Future Research Directions 231References 23312 Context-Based Social Media Recommendation System 237R. Sujithra Kanmani and B. Surendiran12.1 Introduction 23712.2 Literature Survey 24012.3 Motivation and Objectives 24112.3.1 Architecture 24112.3.2 Modules 24212.3.3 Implementation Details 24312.4 Performance Measures 24312.5 Precision 24312.6 Recall 24312.7 F- Measure 24412.8 Evaluation Results 24412.9 Conclusion and Future Work 247References 24813 Netflix Challenge—Improving Movie Recommendations 251Vasu Goel13.1 Introduction 25113.2 Data Preprocessing 25213.3 MovieLens Data 25313.4 Data Exploration 25513.5 Distributions 25613.6 Data Analysis 25713.7 Results 26513.8 Conclusion 266References 26614 Product or Item-Based Recommender System 269Jyoti Rani, Usha Mittal and Geetika Gupta14.1 Introduction 27014.2 Various Techniques to Design Food Recommendation System 27114.2.1 Collaborative Filtering Recommender Systems 27114.2.2 Content-Based Recommender Systems (CB) 27214.2.3 Knowledge-Based Recommender Systems 27214.2.4 Hybrid Recommender Systems 27314.2.5 Context Aware Approaches 27314.2.6 Group-Based Methods 27314.2.7 Different Types of Food Recommender Systems 27314.3 Implementation of Food Recommender System Using Content-Based Approach 27614.3.1 Item Profile Representation 27714.3.2 Information Retrieval 27814.3.3 Word2vec 27814.3.4 How are word2vec Embedding’s Obtained? 27814.3.5 Obtaining word2vec Embeddings 27914.3.6 Dataset 28014.3.6.1 Data Preprocessing 28014.3.7 Web Scrapping For Food List 28014.3.7.1 Porter Stemming All Words 28014.3.7.2 Filtering Our Ingredients 28014.3.7.3 Final Data Frame with Dishes and Their Ingredients 28114.3.7.4 Hamming Distance 28114.3.7.5 Jaccard Distance 28214.4 Results 28214.5 Observations 28314.6 Future Perspective of Recommender Systems 28314.6.1 User Information Challenges 28314.6.1.1 User Nutrition Information Uncertainty 28314.6.1.2 User Rating Data Collection 28414.6.2 Recommendation Algorithms Challenges 28414.6.2.1 User Information Such as Likes/ Dislikes Food or Nutritional Needs 28414.6.2.2 Recipe Databases 28414.6.2.3 A Set of Constraints or Rules 28514.6.3 Challenges Concerning Changing Eating Behavior of Consumers 28514.6.4 Challenges Regarding Explanations and Visualizations 28614.7 Conclusion 286Acknowledgements 287References 287Part 4: Blockchain & IoT-Based Recommender Systems 29115 A Trust-Based Recommender System Built on IoT Blockchain Network With Cognitive Framework 293S. Porkodi and D. Kesavaraja15.1 Introduction 29415.1.1 Today and Tomorrow 29415.1.2 Vision 29415.1.3 Internet of Things 29415.1.4 Blockchain 29515.1.5 Cognitive Systems 29615.1.6 Application 29615.2 Technologies and its Combinations 29715.2.1 IoT–Blockchain 29715.2.2 IoT–Cognitive System 29815.2.3 Blockchain–Cognitive System 29815.2.4 IoT–Blockchain–Cognitive System 29815.3 Crypto Currencies With IoT–Case Studies 29915.4 Trust-Based Recommender System 29915.4.1 Requirement 29915.4.2 Things Management 30215.4.3 Cognitive Process 30315.5 Recommender System Platform 30415.6 Conclusion and Future Directions 307References 30716 Development of a Recommender System HealthMudra Using Blockchain for Prevention of Diabetes 313Rashmi Bhardwaj and Debabrata Datta16.1 Introduction 31416.2 Architecture of Blockchain 31716.2.1 Definition of Blockchain 31816.2.2 Structure of Blockchain 31816.3 Role of HealthMudra in Diabetic 32216.4 Blockchain Technology Solutions 32416.4.1 Predictive Models of Health Data Analysis 32516.5 Conclusions 325References 326Part 5: Healthcare Recommender Systems 32917 Case Study 1: Health Care Recommender Systems 331Usha Mittal, Nancy Singla and Geetika Gupta17.1 Introduction 33217.1.1 Health Care Recommender System 33217.1.2 Parkinson’s Disease: Causes and Symptoms 33317.1.3 Parkinson’s Disease: Treatment and Surgical Approaches 33417.2 Review of Literature 33517.2.1 Machine Learning Algorithms for Parkinson’s Data 33717.2.2 Visualization 34017.3 Recommender System for Parkinson’s Disease (PD) 34117.3.1 How Will One Know When Parkinson’s has Progressed? 34217.3.2 Dataset for Parkinson’s Disease (PD) 34217.3.3 Feature Selection 34317.3.4 Classification 34317.3.4.1 Logistic Regression 34317.3.4.2 K Nearest Neighbor (KNN) 34317.3.4.3 Support Vector Machine (SVM) 34417.3.4.4 Decision Tree 34417.3.5 Train and Test Data 34417.3.6 Recommender System 34417.4 Future Perspectives 34517.5 Conclusions 346References 34818 Temporal Change Analysis-Based Recommender System for Alzheimer Disease Classification 351S. Naganandhini, P. Shanmugavadivu and M. Mary Shanthi Rani18.1 Introduction 35218.2 Related Work 35218.3 Mechanism of TCA-RS-AD 35318.4 Experimental Dataset 35418.5 Neural Network 35718.6 Conclusion 370References 37019 Regularization of Graphs: Sentiment Classification 373R.S.M. Lakshmi Patibandla19.1 Introduction 37319.2 Neural Structured Learning 37419.3 Some Neural Network Models 37519.4 Experimental Results 37719.4.1 Base Model 37919.4.2 Graph Regularization 38219.5 Conclusion 383References 38420 TSARS: A Tree-Similarity Algorithm-Based Agricultural Recommender System 387Madhusree Kuanr, Puspanjali Mohapatra and Sasmita Subhadarsinee Choudhury20.1 Introduction 38820.2 Literature Survey 39020.3 Research Gap 39320.4 Problem Definitions 39320.5 Methodology 39320.6 Results & Discussion 39420.6.1 Performance Evaluation 39420.6.2 Time Complexity Analysis 39620.7 Conclusion & Future Work 397References 39921 Influenceable Targets Recommendation Analyzing Social Activities in Egocentric Online Social Networks 401Soumyadeep Debnath, Dhrubasish Sarkar and Dipankar Das21.1 Introduction 40221.2 Literature Review 40321.3 Dataset Collection Process with Details 40421.3.1 Main User’s Activities Data 40521.3.2 Network Member’s Activities Data 40521.3.3 Tools and Libraries for Data Collection 40521.3.4 Details of the Datasets 40621.4 Primary Preprocessing of Data 40621.4.1 Language Detection and Translation 40621.4.2 Tagged Tweeters Collection 40721.4.3 Textual Noise Removal 40721.4.4 Textual Spelling and Correction 40721.5 Influence and Social Activities Analysis 40721.5.1 Step 1: Targets Selection From OSMs 40821.5.2 Step 3: Categories Classification of Social Contents 40821.5.3 Step 4: Sentiments Analysis of Social Contents 40821.6 Recommendation System 40921.6.1 Secondary Preprocessing of Data 40921.6.2 Recommendation Analyzing Contents of Social Activities 41121.7 Top Most Influenceable Targets Evaluation 41321.8 Conclusion 41421.9 Future Scope 415References 415Index 417

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.

• Preface xixAcknowledgment xxiiiPart 1: Introduction to Recommender Systems 11 An Introduction to Basic Concepts on Recommender Systems 3Pooja Rana, Nishi Jain and Usha Mittal1.1 Introduction 41.2 Functions of Recommendation Systems 51.3 Data and Knowledge Sources 61.4 Types of Recommendation Systems 81.4.1 Content-Based 81.4.1.1 Advantages of Content-Based Recommendation 111.4.1.2 Disadvantages of Content-Based Recommendation 111.4.2 Collaborative Filtering 121.5 Item-Based Recommendation vs. User-Based Recommendation System 141.5.1 Advantages of Memory-Based Collaborative Filtering 151.5.2 Shortcomings 161.5.3 Advantages of Model-Based Collaborative Filtering 171.5.4 Shortcomings 171.5.5 Hybrid Recommendation System 171.5.6 Advantages of Hybrid Recommendation Systems 181.5.7 Shortcomings 181.5.8 Other Recommendation Systems 181.6 Evaluation Metrics for Recommendation Engines 191.7 Problems with Recommendation Systems and Possible Solutions 201.7.1 Advantages of Recommendation Systems 231.7.2 Disadvantages of Recommendation Systems 241.8 Applications of Recommender Systems 24References 252 A Brief Model Overview of Personalized Recommendation to Citizens in the Health-Care Industry 27Subhasish Mohapatra and Kunal Anand2.1 Introduction 282.2 Methods Used in Recommender System 292.2.1 Content-Based 292.2.2 Collaborative Filtering 322.2.3 Hybrid Filtering 332.3 Related Work 332.4 Types of Explanation 342.5 Explanation Methodology 352.5.1 Collaborative-Based 362.5.2 Content-Based 362.5.3 Knowledge and Utility-Based 372.5.4 Case-Based 372.5.5 Demographic-Based 382.6 Proposed Theoretical Framework for Explanation-Based Recommender System in Health-Care Domain 392.7 Flowchart 392.8 Conclusion 41References 413 2Es of TIS: A Review of Information Exchange and Extraction in Tourism Information Systems 45Malik M. Saad Missen, Mickaël Coustaty, Hina Asmat, Amnah Firdous, Nadeem Akhtar, Muhammad Akram and V. B. Surya Prasath3.1 Introduction 463.2 Information Exchange 493.2.1 Exchange of Tourism Objects Data 493.2.1.1 Semantic Clashes 503.2.1.2 Structural Clashes 503.2.2 Schema.org—The Future 513.2.2.1 Schema.org Extension Mechanism 523.2.2.2 Schema.org Tourism Vocabulary 523.2.3 Exchange of Tourism-Related Statistical Data 533.3 Information Extraction 553.3.1 Opinion Extraction 563.3.2 Opinion Mining 573.4 Sentiment Annotation 573.4.1 SentiML 583.4.1.1 SentiML Example 583.4.2 OpinionMiningML 593.4.2.1 OpinionMiningML Example 603.4.3 EmotionML 613.4.3.1 EmotionML Example 613.5 Comparison of Different Annotations Schemes 623.6 Temporal and Event Extraction 643.7 TimeML 653.8 Conclusions 67References 67Part 2: Machine Learning-Based Recommender Systems 714 Concepts of Recommendation System from the Perspective of Machine Learning 73Sumanta Chandra Mishra Sharma, Adway Mitra and Deepayan Chakraborty4.1 Introduction 734.2 Entities of Recommendation System 744.2.1 User 744.2.2 Items 754.2.3 Action 754.3 Techniques of Recommendation 764.3.1 Personalized Recommendation System 774.3.2 Non-Personalized Recommendation System 774.3.3 Content-Based Filtering 774.3.4 Collaborative Filtering 784.3.5 Model-Based Filtering 804.3.6 Memory-Based Filtering 804.3.7 Hybrid Recommendation Technique 814.3.8 Social Media Recommendation Technique 824.4 Performance Evaluation 824.5 Challenges 834.5.1 Sparsity of Data 844.5.2 Scalability 844.5.3 Slow Start 844.5.4 Gray Sheep and Black Sheep 844.5.5 Item Duplication 844.5.6 Privacy Issue 844.5.7 Biasness 854.6 Applications 854.7 Conclusion 85References 855 A Machine Learning Approach to Recommend Suitable Crops and Fertilizers for Agriculture 89Govind Kumar Jha, Preetish Ranjan and Manish Gaur5.1 Introduction 905.2 Literature Review 915.3 Methodology 935.4 Results and Analysis 965.5 Conclusion 97References 986 Accuracy-Assured Privacy-Preserving Recommender System Using Hybrid-Based Deep Learning Method 101Abhaya Kumar Sahoo and Chittaranjan Pradhan6.1 Introduction 1026.2 Overview of Recommender System 1036.3 Collaborative Filtering-Based Recommender System 1066.4 Machine Learning Methods Used in Recommender System 1076.5 Proposed RBM Model-Based Movie Recommender System 1106.6 Proposed CRBM Model-Based Movie Recommender System 1136.7 Conclusion and Future Work 115References 1187 Machine Learning-Based Recommender System for Breast Cancer Prognosis 121G. Kanimozhi, P. Shanmugavadivu and M. Mary Shanthi Rani7.1 Introduction 1227.2 Related Works 1247.3 Methodology 1257.3.1 Experimental Dataset 1257.3.2 Feature Selection 1277.3.3 Functional Phases of MLRS-BC 1287.3.4 Prediction Algorithms 1297.4 Results and Discussion 1317.5 Conclusion 138Acknowledgment 139References 1398 A Recommended System for Crop Disease Detection and Yield Prediction Using Machine Learning Approach 141Pooja Akulwar8.1 Introduction 1428.2 Machine Learning 1438.2.1 Overview 1438.2.2 Machine Learning Algorithms 1458.2.3 Machine Learning Methods 1468.2.3.1 Artificial Neural Network 1468.2.3.2 Support Vector Machines 1468.2.3.3 K-Nearest Neighbors (K-NN) 1478.2.3.4 Decision Tree Learning 1478.2.3.5 Random Forest 1488.2.3.6 Gradient Boosted Decision Tree (GBDT) 1498.2.3.7 Regularized Greedy Forest (RGF) 1508.3 Recommender System 1518.3.1 Overview 1518.4 Crop Management 1538.4.1 Yield Prediction 1538.4.2 Disease Detection 1548.4.3 Weed Detection 1568.4.4 Crop Quality 1598.5 Application—Crop Disease Detection and Yield Prediction 159References 162Part 3: Content-Based Recommender Systems 1659 Content-Based Recommender Systems 167Poonam Bhatia Anand and Rajender Nath9.1 Introduction 1679.2 Literature Review 1689.3 Recommendation Process 1729.3.1 Architecture of Content-Based Recommender System 1729.3.2 Profile Cleaner Representation 1759.4 Techniques Used for Item Representation and Learning User Profile 1769.4.1 Representation of Content 1769.4.2 Vector Space Model Based on Keywords 1779.4.3 Techniques for Learning Profiles of User 1799.4.3.1 Probabilistic Method 1799.4.3.2 Rocchio’s and Relevance Feedback Method 1809.4.3.3 Other Methods 1819.5 Applicability of Recommender System in Healthcare and Agriculture 1829.5.1 Recommendation System in Healthcare 1829.5.2 Recommender System in Agriculture 1849.6 Pros and Cons of Content-Based Recommender System 1869.7 Conclusion 187References 18810 Content (Item)-Based Recommendation System 197R. Balamurali10.1 Introduction 19810.2 Phases of Content-Based Recommendation Generation 19810.3 Content-Based Recommendation Using Cosine Similarity 19910.4 Content-Based Recommendations Using Optimization Techniques 20410.5 Content-Based Recommendation Using the Tree Induction Algorithm 20810.6 Summary 212References 21311 Content-Based Health Recommender Systems 215Soumya Prakash Rana, Maitreyee Dey, Javier Prieto and Sandra Dudley11.1 Introduction 21611.2 Typical Health Recommender System Framework 21711.3 Components of Content-Based Health Recommender System 21811.4 Unstructured Data Processing 22011.5 Unsupervised Feature Extraction & Weighting 22111.5.1 Bag of Words (BoW) 22111.5.2 Word to Vector (Word2Vec) 22211.5.3 Global Vectors for Word Representations (Glove) 22211.6 Supervised Feature Selection & Weighting 22211.7 Feedback Collection 22511.7.1 Medication & Therapy 22511.7.2 Healthy Diet Plan 22511.7.3 Suggestions 22511.8 Training & Health Recommendation Generation 22611.8.1 Analogy-Based ML in CBHRS 22711.8.2 Specimen-Based ML in CBHRS 22711.9 Evaluation of Content Based Health Recommender System 22811.10 Design Criteria of CBHRS 22911.10.1 Micro-Level & Lucidity 23011.10.2 Interactive Interface 23011.10.3 Data Protection 23011.10.4 Risk & Uncertainty Management 23111.10.5 Doctor-in-Loop (DiL) 23111.11 Conclusions and Future Research Directions 231References 23312 Context-Based Social Media Recommendation System 237R. Sujithra Kanmani and B. Surendiran12.1 Introduction 23712.2 Literature Survey 24012.3 Motivation and Objectives 24112.3.1 Architecture 24112.3.2 Modules 24212.3.3 Implementation Details 24312.4 Performance Measures 24312.5 Precision 24312.6 Recall 24312.7 F- Measure 24412.8 Evaluation Results 24412.9 Conclusion and Future Work 247References 24813 Netflix Challenge—Improving Movie Recommendations 251Vasu Goel13.1 Introduction 25113.2 Data Preprocessing 25213.3 MovieLens Data 25313.4 Data Exploration 25513.5 Distributions 25613.6 Data Analysis 25713.7 Results 26513.8 Conclusion 266References 26614 Product or Item-Based Recommender System 269Jyoti Rani, Usha Mittal and Geetika Gupta14.1 Introduction 27014.2 Various Techniques to Design Food Recommendation System 27114.2.1 Collaborative Filtering Recommender Systems 27114.2.2 Content-Based Recommender Systems (CB) 27214.2.3 Knowledge-Based Recommender Systems 27214.2.4 Hybrid Recommender Systems 27314.2.5 Context Aware Approaches 27314.2.6 Group-Based Methods 27314.2.7 Different Types of Food Recommender Systems 27314.3 Implementation of Food Recommender System Using Content-Based Approach 27614.3.1 Item Profile Representation 27714.3.2 Information Retrieval 27814.3.3 Word2vec 27814.3.4 How are word2vec Embedding’s Obtained? 27814.3.5 Obtaining word2vec Embeddings 27914.3.6 Dataset 28014.3.6.1 Data Preprocessing 28014.3.7 Web Scrapping For Food List 28014.3.7.1 Porter Stemming All Words 28014.3.7.2 Filtering Our Ingredients 28014.3.7.3 Final Data Frame with Dishes and Their Ingredients 28114.3.7.4 Hamming Distance 28114.3.7.5 Jaccard Distance 28214.4 Results 28214.5 Observations 28314.6 Future Perspective of Recommender Systems 28314.6.1 User Information Challenges 28314.6.1.1 User Nutrition Information Uncertainty 28314.6.1.2 User Rating Data Collection 28414.6.2 Recommendation Algorithms Challenges 28414.6.2.1 User Information Such as Likes/ Dislikes Food or Nutritional Needs 28414.6.2.2 Recipe Databases 28414.6.2.3 A Set of Constraints or Rules 28514.6.3 Challenges Concerning Changing Eating Behavior of Consumers 28514.6.4 Challenges Regarding Explanations and Visualizations 28614.7 Conclusion 286Acknowledgements 287References 287Part 4: Blockchain & IoT-Based Recommender Systems 29115 A Trust-Based Recommender System Built on IoT Blockchain Network With Cognitive Framework 293S. Porkodi and D. Kesavaraja15.1 Introduction 29415.1.1 Today and Tomorrow 29415.1.2 Vision 29415.1.3 Internet of Things 29415.1.4 Blockchain 29515.1.5 Cognitive Systems 29615.1.6 Application 29615.2 Technologies and its Combinations 29715.2.1 IoT–Blockchain 29715.2.2 IoT–Cognitive System 29815.2.3 Blockchain–Cognitive System 29815.2.4 IoT–Blockchain–Cognitive System 29815.3 Crypto Currencies With IoT–Case Studies 29915.4 Trust-Based Recommender System 29915.4.1 Requirement 29915.4.2 Things Management 30215.4.3 Cognitive Process 30315.5 Recommender System Platform 30415.6 Conclusion and Future Directions 307References 30716 Development of a Recommender System HealthMudra Using Blockchain for Prevention of Diabetes 313Rashmi Bhardwaj and Debabrata Datta16.1 Introduction 31416.2 Architecture of Blockchain 31716.2.1 Definition of Blockchain 31816.2.2 Structure of Blockchain 31816.3 Role of HealthMudra in Diabetic 32216.4 Blockchain Technology Solutions 32416.4.1 Predictive Models of Health Data Analysis 32516.5 Conclusions 325References 326Part 5: Healthcare Recommender Systems 32917 Case Study 1: Health Care Recommender Systems 331Usha Mittal, Nancy Singla and Geetika Gupta17.1 Introduction 33217.1.1 Health Care Recommender System 33217.1.2 Parkinson’s Disease: Causes and Symptoms 33317.1.3 Parkinson’s Disease: Treatment and Surgical Approaches 33417.2 Review of Literature 33517.2.1 Machine Learning Algorithms for Parkinson’s Data 33717.2.2 Visualization 34017.3 Recommender System for Parkinson’s Disease (PD) 34117.3.1 How Will One Know When Parkinson’s has Progressed? 34217.3.2 Dataset for Parkinson’s Disease (PD) 34217.3.3 Feature Selection 34317.3.4 Classification 34317.3.4.1 Logistic Regression 34317.3.4.2 K Nearest Neighbor (KNN) 34317.3.4.3 Support Vector Machine (SVM) 34417.3.4.4 Decision Tree 34417.3.5 Train and Test Data 34417.3.6 Recommender System 34417.4 Future Perspectives 34517.5 Conclusions 346References 34818 Temporal Change Analysis-Based Recommender System for Alzheimer Disease Classification 351S. Naganandhini, P. Shanmugavadivu and M. Mary Shanthi Rani18.1 Introduction 35218.2 Related Work 35218.3 Mechanism of TCA-RS-AD 35318.4 Experimental Dataset 35418.5 Neural Network 35718.6 Conclusion 370References 37019 Regularization of Graphs: Sentiment Classification 373R.S.M. Lakshmi Patibandla19.1 Introduction 37319.2 Neural Structured Learning 37419.3 Some Neural Network Models 37519.4 Experimental Results 37719.4.1 Base Model 37919.4.2 Graph Regularization 38219.5 Conclusion 383References 38420 TSARS: A Tree-Similarity Algorithm-Based Agricultural Recommender System 387Madhusree Kuanr, Puspanjali Mohapatra and Sasmita Subhadarsinee Choudhury20.1 Introduction 38820.2 Literature Survey 39020.3 Research Gap 39320.4 Problem Definitions 39320.5 Methodology 39320.6 Results & Discussion 39420.6.1 Performance Evaluation 39420.6.2 Time Complexity Analysis 39620.7 Conclusion & Future Work 397References 39921 Influenceable Targets Recommendation Analyzing Social Activities in Egocentric Online Social Networks 401Soumyadeep Debnath, Dhrubasish Sarkar and Dipankar Das21.1 Introduction 40221.2 Literature Review 40321.3 Dataset Collection Process with Details 40421.3.1 Main User’s Activities Data 40521.3.2 Network Member’s Activities Data 40521.3.3 Tools and Libraries for Data Collection 40521.3.4 Details of the Datasets 40621.4 Primary Preprocessing of Data 40621.4.1 Language Detection and Translation 40621.4.2 Tagged Tweeters Collection 40721.4.3 Textual Noise Removal 40721.4.4 Textual Spelling and Correction 40721.5 Influence and Social Activities Analysis 40721.5.1 Step 1: Targets Selection From OSMs 40821.5.2 Step 3: Categories Classification of Social Contents 40821.5.3 Step 4: Sentiments Analysis of Social Contents 40821.6 Recommendation System 40921.6.1 Secondary Preprocessing of Data 40921.6.2 Recommendation Analyzing Contents of Social Activities 41121.7 Top Most Influenceable Targets Evaluation 41321.8 Conclusion 41421.9 Future Scope 415References 415Index 417

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.

• Preface xixAcknowledgment xxiiiPart 1: Introduction to Recommender Systems 11 An Introduction to Basic Concepts on Recommender Systems 3Pooja Rana, Nishi Jain and Usha Mittal1.1 Introduction 41.2 Functions of Recommendation Systems 51.3 Data and Knowledge Sources 61.4 Types of Recommendation Systems 81.4.1 Content-Based 81.4.1.1 Advantages of Content-Based Recommendation 111.4.1.2 Disadvantages of Content-Based Recommendation 111.4.2 Collaborative Filtering 121.5 Item-Based Recommendation vs. User-Based Recommendation System 141.5.1 Advantages of Memory-Based Collaborative Filtering 151.5.2 Shortcomings 161.5.3 Advantages of Model-Based Collaborative Filtering 171.5.4 Shortcomings 171.5.5 Hybrid Recommendation System 171.5.6 Advantages of Hybrid Recommendation Systems 181.5.7 Shortcomings 181.5.8 Other Recommendation Systems 181.6 Evaluation Metrics for Recommendation Engines 191.7 Problems with Recommendation Systems and Possible Solutions 201.7.1 Advantages of Recommendation Systems 231.7.2 Disadvantages of Recommendation Systems 241.8 Applications of Recommender Systems 24References 252 A Brief Model Overview of Personalized Recommendation to Citizens in the Health-Care Industry 27Subhasish Mohapatra and Kunal Anand2.1 Introduction 282.2 Methods Used in Recommender System 292.2.1 Content-Based 292.2.2 Collaborative Filtering 322.2.3 Hybrid Filtering 332.3 Related Work 332.4 Types of Explanation 342.5 Explanation Methodology 352.5.1 Collaborative-Based 362.5.2 Content-Based 362.5.3 Knowledge and Utility-Based 372.5.4 Case-Based 372.5.5 Demographic-Based 382.6 Proposed Theoretical Framework for Explanation-Based Recommender System in Health-Care Domain 392.7 Flowchart 392.8 Conclusion 41References 413 2Es of TIS: A Review of Information Exchange and Extraction in Tourism Information Systems 45Malik M. Saad Missen, Mickaël Coustaty, Hina Asmat, Amnah Firdous, Nadeem Akhtar, Muhammad Akram and V. B. Surya Prasath3.1 Introduction 463.2 Information Exchange 493.2.1 Exchange of Tourism Objects Data 493.2.1.1 Semantic Clashes 503.2.1.2 Structural Clashes 503.2.2 Schema.org—The Future 513.2.2.1 Schema.org Extension Mechanism 523.2.2.2 Schema.org Tourism Vocabulary 523.2.3 Exchange of Tourism-Related Statistical Data 533.3 Information Extraction 553.3.1 Opinion Extraction 563.3.2 Opinion Mining 573.4 Sentiment Annotation 573.4.1 SentiML 583.4.1.1 SentiML Example 583.4.2 OpinionMiningML 593.4.2.1 OpinionMiningML Example 603.4.3 EmotionML 613.4.3.1 EmotionML Example 613.5 Comparison of Different Annotations Schemes 623.6 Temporal and Event Extraction 643.7 TimeML 653.8 Conclusions 67References 67Part 2: Machine Learning-Based Recommender Systems 714 Concepts of Recommendation System from the Perspective of Machine Learning 73Sumanta Chandra Mishra Sharma, Adway Mitra and Deepayan Chakraborty4.1 Introduction 734.2 Entities of Recommendation System 744.2.1 User 744.2.2 Items 754.2.3 Action 754.3 Techniques of Recommendation 764.3.1 Personalized Recommendation System 774.3.2 Non-Personalized Recommendation System 774.3.3 Content-Based Filtering 774.3.4 Collaborative Filtering 784.3.5 Model-Based Filtering 804.3.6 Memory-Based Filtering 804.3.7 Hybrid Recommendation Technique 814.3.8 Social Media Recommendation Technique 824.4 Performance Evaluation 824.5 Challenges 834.5.1 Sparsity of Data 844.5.2 Scalability 844.5.3 Slow Start 844.5.4 Gray Sheep and Black Sheep 844.5.5 Item Duplication 844.5.6 Privacy Issue 844.5.7 Biasness 854.6 Applications 854.7 Conclusion 85References 855 A Machine Learning Approach to Recommend Suitable Crops and Fertilizers for Agriculture 89Govind Kumar Jha, Preetish Ranjan and Manish Gaur5.1 Introduction 905.2 Literature Review 915.3 Methodology 935.4 Results and Analysis 965.5 Conclusion 97References 986 Accuracy-Assured Privacy-Preserving Recommender System Using Hybrid-Based Deep Learning Method 101Abhaya Kumar Sahoo and Chittaranjan Pradhan6.1 Introduction 1026.2 Overview of Recommender System 1036.3 Collaborative Filtering-Based Recommender System 1066.4 Machine Learning Methods Used in Recommender System 1076.5 Proposed RBM Model-Based Movie Recommender System 1106.6 Proposed CRBM Model-Based Movie Recommender System 1136.7 Conclusion and Future Work 115References 1187 Machine Learning-Based Recommender System for Breast Cancer Prognosis 121G. Kanimozhi, P. Shanmugavadivu and M. Mary Shanthi Rani7.1 Introduction 1227.2 Related Works 1247.3 Methodology 1257.3.1 Experimental Dataset 1257.3.2 Feature Selection 1277.3.3 Functional Phases of MLRS-BC 1287.3.4 Prediction Algorithms 1297.4 Results and Discussion 1317.5 Conclusion 138Acknowledgment 139References 1398 A Recommended System for Crop Disease Detection and Yield Prediction Using Machine Learning Approach 141Pooja Akulwar8.1 Introduction 1428.2 Machine Learning 1438.2.1 Overview 1438.2.2 Machine Learning Algorithms 1458.2.3 Machine Learning Methods 1468.2.3.1 Artificial Neural Network 1468.2.3.2 Support Vector Machines 1468.2.3.3 K-Nearest Neighbors (K-NN) 1478.2.3.4 Decision Tree Learning 1478.2.3.5 Random Forest 1488.2.3.6 Gradient Boosted Decision Tree (GBDT) 1498.2.3.7 Regularized Greedy Forest (RGF) 1508.3 Recommender System 1518.3.1 Overview 1518.4 Crop Management 1538.4.1 Yield Prediction 1538.4.2 Disease Detection 1548.4.3 Weed Detection 1568.4.4 Crop Quality 1598.5 Application—Crop Disease Detection and Yield Prediction 159References 162Part 3: Content-Based Recommender Systems 1659 Content-Based Recommender Systems 167Poonam Bhatia Anand and Rajender Nath9.1 Introduction 1679.2 Literature Review 1689.3 Recommendation Process 1729.3.1 Architecture of Content-Based Recommender System 1729.3.2 Profile Cleaner Representation 1759.4 Techniques Used for Item Representation and Learning User Profile 1769.4.1 Representation of Content 1769.4.2 Vector Space Model Based on Keywords 1779.4.3 Techniques for Learning Profiles of User 1799.4.3.1 Probabilistic Method 1799.4.3.2 Rocchio’s and Relevance Feedback Method 1809.4.3.3 Other Methods 1819.5 Applicability of Recommender System in Healthcare and Agriculture 1829.5.1 Recommendation System in Healthcare 1829.5.2 Recommender System in Agriculture 1849.6 Pros and Cons of Content-Based Recommender System 1869.7 Conclusion 187References 18810 Content (Item)-Based Recommendation System 197R. Balamurali10.1 Introduction 19810.2 Phases of Content-Based Recommendation Generation 19810.3 Content-Based Recommendation Using Cosine Similarity 19910.4 Content-Based Recommendations Using Optimization Techniques 20410.5 Content-Based Recommendation Using the Tree Induction Algorithm 20810.6 Summary 212References 21311 Content-Based Health Recommender Systems 215Soumya Prakash Rana, Maitreyee Dey, Javier Prieto and Sandra Dudley11.1 Introduction 21611.2 Typical Health Recommender System Framework 21711.3 Components of Content-Based Health Recommender System 21811.4 Unstructured Data Processing 22011.5 Unsupervised Feature Extraction & Weighting 22111.5.1 Bag of Words (BoW) 22111.5.2 Word to Vector (Word2Vec) 22211.5.3 Global Vectors for Word Representations (Glove) 22211.6 Supervised Feature Selection & Weighting 22211.7 Feedback Collection 22511.7.1 Medication & Therapy 22511.7.2 Healthy Diet Plan 22511.7.3 Suggestions 22511.8 Training & Health Recommendation Generation 22611.8.1 Analogy-Based ML in CBHRS 22711.8.2 Specimen-Based ML in CBHRS 22711.9 Evaluation of Content Based Health Recommender System 22811.10 Design Criteria of CBHRS 22911.10.1 Micro-Level & Lucidity 23011.10.2 Interactive Interface 23011.10.3 Data Protection 23011.10.4 Risk & Uncertainty Management 23111.10.5 Doctor-in-Loop (DiL) 23111.11 Conclusions and Future Research Directions 231References 23312 Context-Based Social Media Recommendation System 237R. Sujithra Kanmani and B. Surendiran12.1 Introduction 23712.2 Literature Survey 24012.3 Motivation and Objectives 24112.3.1 Architecture 24112.3.2 Modules 24212.3.3 Implementation Details 24312.4 Performance Measures 24312.5 Precision 24312.6 Recall 24312.7 F- Measure 24412.8 Evaluation Results 24412.9 Conclusion and Future Work 247References 24813 Netflix Challenge—Improving Movie Recommendations 251Vasu Goel13.1 Introduction 25113.2 Data Preprocessing 25213.3 MovieLens Data 25313.4 Data Exploration 25513.5 Distributions 25613.6 Data Analysis 25713.7 Results 26513.8 Conclusion 266References 26614 Product or Item-Based Recommender System 269Jyoti Rani, Usha Mittal and Geetika Gupta14.1 Introduction 27014.2 Various Techniques to Design Food Recommendation System 27114.2.1 Collaborative Filtering Recommender Systems 27114.2.2 Content-Based Recommender Systems (CB) 27214.2.3 Knowledge-Based Recommender Systems 27214.2.4 Hybrid Recommender Systems 27314.2.5 Context Aware Approaches 27314.2.6 Group-Based Methods 27314.2.7 Different Types of Food Recommender Systems 27314.3 Implementation of Food Recommender System Using Content-Based Approach 27614.3.1 Item Profile Representation 27714.3.2 Information Retrieval 27814.3.3 Word2vec 27814.3.4 How are word2vec Embedding’s Obtained? 27814.3.5 Obtaining word2vec Embeddings 27914.3.6 Dataset 28014.3.6.1 Data Preprocessing 28014.3.7 Web Scrapping For Food List 28014.3.7.1 Porter Stemming All Words 28014.3.7.2 Filtering Our Ingredients 28014.3.7.3 Final Data Frame with Dishes and Their Ingredients 28114.3.7.4 Hamming Distance 28114.3.7.5 Jaccard Distance 28214.4 Results 28214.5 Observations 28314.6 Future Perspective of Recommender Systems 28314.6.1 User Information Challenges 28314.6.1.1 User Nutrition Information Uncertainty 28314.6.1.2 User Rating Data Collection 28414.6.2 Recommendation Algorithms Challenges 28414.6.2.1 User Information Such as Likes/ Dislikes Food or Nutritional Needs 28414.6.2.2 Recipe Databases 28414.6.2.3 A Set of Constraints or Rules 28514.6.3 Challenges Concerning Changing Eating Behavior of Consumers 28514.6.4 Challenges Regarding Explanations and Visualizations 28614.7 Conclusion 286Acknowledgements 287References 287Part 4: Blockchain & IoT-Based Recommender Systems 29115 A Trust-Based Recommender System Built on IoT Blockchain Network With Cognitive Framework 293S. Porkodi and D. Kesavaraja15.1 Introduction 29415.1.1 Today and Tomorrow 29415.1.2 Vision 29415.1.3 Internet of Things 29415.1.4 Blockchain 29515.1.5 Cognitive Systems 29615.1.6 Application 29615.2 Technologies and its Combinations 29715.2.1 IoT–Blockchain 29715.2.2 IoT–Cognitive System 29815.2.3 Blockchain–Cognitive System 29815.2.4 IoT–Blockchain–Cognitive System 29815.3 Crypto Currencies With IoT–Case Studies 29915.4 Trust-Based Recommender System 29915.4.1 Requirement 29915.4.2 Things Management 30215.4.3 Cognitive Process 30315.5 Recommender System Platform 30415.6 Conclusion and Future Directions 307References 30716 Development of a Recommender System HealthMudra Using Blockchain for Prevention of Diabetes 313Rashmi Bhardwaj and Debabrata Datta16.1 Introduction 31416.2 Architecture of Blockchain 31716.2.1 Definition of Blockchain 31816.2.2 Structure of Blockchain 31816.3 Role of HealthMudra in Diabetic 32216.4 Blockchain Technology Solutions 32416.4.1 Predictive Models of Health Data Analysis 32516.5 Conclusions 325References 326Part 5: Healthcare Recommender Systems 32917 Case Study 1: Health Care Recommender Systems 331Usha Mittal, Nancy Singla and Geetika Gupta17.1 Introduction 33217.1.1 Health Care Recommender System 33217.1.2 Parkinson’s Disease: Causes and Symptoms 33317.1.3 Parkinson’s Disease: Treatment and Surgical Approaches 33417.2 Review of Literature 33517.2.1 Machine Learning Algorithms for Parkinson’s Data 33717.2.2 Visualization 34017.3 Recommender System for Parkinson’s Disease (PD) 34117.3.1 How Will One Know When Parkinson’s has Progressed? 34217.3.2 Dataset for Parkinson’s Disease (PD) 34217.3.3 Feature Selection 34317.3.4 Classification 34317.3.4.1 Logistic Regression 34317.3.4.2 K Nearest Neighbor (KNN) 34317.3.4.3 Support Vector Machine (SVM) 34417.3.4.4 Decision Tree 34417.3.5 Train and Test Data 34417.3.6 Recommender System 34417.4 Future Perspectives 34517.5 Conclusions 346References 34818 Temporal Change Analysis-Based Recommender System for Alzheimer Disease Classification 351S. Naganandhini, P. Shanmugavadivu and M. Mary Shanthi Rani18.1 Introduction 35218.2 Related Work 35218.3 Mechanism of TCA-RS-AD 35318.4 Experimental Dataset 35418.5 Neural Network 35718.6 Conclusion 370References 37019 Regularization of Graphs: Sentiment Classification 373R.S.M. Lakshmi Patibandla19.1 Introduction 37319.2 Neural Structured Learning 37419.3 Some Neural Network Models 37519.4 Experimental Results 37719.4.1 Base Model 37919.4.2 Graph Regularization 38219.5 Conclusion 383References 38420 TSARS: A Tree-Similarity Algorithm-Based Agricultural Recommender System 387Madhusree Kuanr, Puspanjali Mohapatra and Sasmita Subhadarsinee Choudhury20.1 Introduction 38820.2 Literature Survey 39020.3 Research Gap 39320.4 Problem Definitions 39320.5 Methodology 39320.6 Results & Discussion 39420.6.1 Performance Evaluation 39420.6.2 Time Complexity Analysis 39620.7 Conclusion & Future Work 397References 39921 Influenceable Targets Recommendation Analyzing Social Activities in Egocentric Online Social Networks 401Soumyadeep Debnath, Dhrubasish Sarkar and Dipankar Das21.1 Introduction 40221.2 Literature Review 40321.3 Dataset Collection Process with Details 40421.3.1 Main User’s Activities Data 40521.3.2 Network Member’s Activities Data 40521.3.3 Tools and Libraries for Data Collection 40521.3.4 Details of the Datasets 40621.4 Primary Preprocessing of Data 40621.4.1 Language Detection and Translation 40621.4.2 Tagged Tweeters Collection 40721.4.3 Textual Noise Removal 40721.4.4 Textual Spelling and Correction 40721.5 Influence and Social Activities Analysis 40721.5.1 Step 1: Targets Selection From OSMs 40821.5.2 Step 3: Categories Classification of Social Contents 40821.5.3 Step 4: Sentiments Analysis of Social Contents 40821.6 Recommendation System 40921.6.1 Secondary Preprocessing of Data 40921.6.2 Recommendation Analyzing Contents of Social Activities 41121.7 Top Most Influenceable Targets Evaluation 41321.8 Conclusion 41421.9 Future Scope 415References 415Index 417