Recommender System with Machine Learning and Artificial Intelligence

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

Sachi Nandan Mohanty Jyotir Moy Chatterjee Sarika Jain Ahmed A. Elngar Priya Gupta(Herausgeber*in)

Wiley (Verlag)

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Erschienen am 9. Juni 2020

448 Seiten

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Inhalt

Cover
Title Page
Copyright Page
Contents
Preface
Acknowledgment
Part 1 Introduction to Recommender Systems
Chapter 1 An Introduction to Basic Concepts on Recommender Systems
1.1 Introduction
1.2 Functions of Recommendation Systems
1.3 Data and Knowledge Sources
1.4 Types of Recommendation Systems
1.4.1 Content-Based
1.4.1.1 Advantages of Content-Based Recommendation
1.4.1.2 Disadvantages of Content-Based Recommendation
1.4.2 Collaborative Filtering
1.5 Item-Based Recommendation vs. User-Based Recommendation System
1.5.1 Advantages of Memory-Based Collaborative Filtering
1.5.2 Shortcomings
1.5.3 Advantages of Model-Based Collaborative Filtering
1.5.4 Shortcomings
1.5.5 Hybrid Recommendation System
1.5.6 Advantages of Hybrid Recommendation Systems
1.5.7 Shortcomings
1.5.8 Other Recommendation Systems
1.6 Evaluation Metrics for Recommendation Engines
1.7 Problems with Recommendation Systems and Possible Solutions
1.7.1 Advantages of Recommendation Systems
1.7.2 Disadvantages of Recommendation Systems
1.8 Applications of Recommender Systems
References
Chapter 2 A Brief Model Overview of Personalized Recommendation to Citizens in the Health-Care Industry
2.1 Introduction
2.2 Methods Used in Recommender System
2.2.1 Content-Based
2.2.2 Collaborative Filtering
2.2.3 Hybrid Filtering
2.3 Related Work
2.4 Types of Explanation
2.5 Explanation Methodology
2.5.1 Collaborative-Based
2.5.2 Content-Based
2.5.3 Knowledge and Utility-Based
2.5.4 Case-Based
2.5.5 Demographic-Based
2.6 Proposed Theoretical Framework for Explanation-Based Recommender System in Health-Care Domain
2.7 Flowchart
2.8 Conclusion
References
Chapter 3 2Es of TIS: A Review of Information Exchange and Extraction in Tourism Information Systems
3.1 Introduction
3.2 Information Exchange
3.2.1 Exchange of Tourism Objects Data
3.2.1.1 Semantic Clashes
3.2.1.2 Structural Clashes
3.2.2 Schema.org-The Future
3.2.2.1 Schema.org Extension Mechanism
3.2.2.2 Schema.org Tourism Vocabulary
3.2.3 Exchange of Tourism-Related Statistical Data
3.3 Information Extraction
3.3.1 Opinion Extraction
3.3.2 Opinion Mining
3.4 Sentiment Annotation
3.4.1 SentiML
3.4.1.1 SentiML Example
3.4.2 OpinionMiningML
3.4.2.1 OpinionMiningML Example
3.4.3 EmotionML
3.4.3.1 EmotionML Example
3.5 Comparison of Different Annotations Schemes
3.6 Temporal and Event Extraction
3.7 TimeML
3.8 Conclusions
References
Part 2 Machine Learning-Based Recommender Systems
Chapter 4 Concepts of Recommendation System from the Perspective of Machine Learning
4.1 Introduction
4.2 Entities of Recommendation System
4.2.1 User
4.2.2 Items
4.2.3 Action
4.3 Techniques of Recommendation
4.3.1 Personalized Recommendation System
4.3.2 Non-Personalized Recommendation System
4.3.3 Content-Based Filtering
4.3.4 Collaborative Filtering
4.3.5 Model-Based Filtering
4.3.6 Memory-Based Filtering
4.3.7 Hybrid Recommendation Technique
4.3.8 Social Media Recommendation Technique
4.4 Performance Evaluation
4.5 Challenges
4.5.1 Sparsity of Data
4.5.2 Scalability
4.5.3 Slow Start
4.5.4 Gray Sheep and Black Sheep
4.5.5 Item Duplication
4.5.6 Privacy Issue
4.5.7 Biasness
4.6 Applications
4.7 Conclusion
References
Chapter 5 A Machine Learning Approach to Recommend Suitable Crops and Fertilizers for Agriculture
5.1 Introduction
5.2 Literature Review
5.3 Methodology
5.4 Results and Analysis
5.5 Conclusion
References
Chapter 6 Accuracy-Assured Privacy-Preserving Recommender System Using HybridBased Deep Learning Method
6.1 Introduction
6.2 Overview of Recommender System
6.3 Collaborative Filtering-Based Recommender System
6.4 Machine Learning Methods Used in Recommender System
6.5 Proposed RBM Model-Based Movie Recommender System
6.6 Proposed CRBM Model-Based Movie Recommender System
6.7 Conclusion and Future Work
References
Chapter 7 Machine Learning-Based Recommender System for Breast Cancer Prognosis
7.1 Introduction
7.2 Related Works
7.3 Methodology
7.3.1 Experimental Dataset
7.3.2 Feature Selection
7.3.3 Functional Phases of MLRS-BC
7.3.4 Prediction Algorithms
7.4 Results and Discussion
7.5 Conclusion
Acknowledgment
References
Chapter 8 A Recommended System for Crop Disease Detection and Yield Prediction Using Machine Learning Approach
8.1 Introduction
8.2 Machine Learning
8.2.1 Overview
8.2.2 Machine Learning Algorithms
8.2.3 Machine Learning Methods
8.2.3.1 Artificial Neural Network
8.2.3.2 Support Vector Machines
8.2.3.3 K-Nearest Neighbors (K-NN)
8.2.3.4 Decision Tree Learning
8.2.3.5 Random Forest
8.2.3.6 Gradient Boosted Decision Tree (GBDT)
8.2.3.7 Regularized Greedy Forest (RGF)
8.3 Recommender System
8.3.1 Overview
8.4 Crop Management
8.4.1 Yield Prediction
8.4.2 Disease Detection
8.4.3 Weed Detection
8.4.4 Crop Quality
8.5 Application-Crop Disease Detection and Yield Prediction
References
Part 3 Content-Based Recommender Systems
Chapter 9 Content-Based Recommender Systems
9.1 Introduction
9.2 Literature Review
9.3 Recommendation Process
9.3.1 Architecture of Content-Based Recommender System
9.3.2 Profile Cleaner Representation
9.4 Techniques Used for Item Representation and Learning User Profile
9.4.1 Representation of Content
9.4.2 Vector Space Model Based on Keywords
9.4.3 Techniques for Learning Profiles of User
9.4.3.1 Probabilistic Method
9.4.3.2 Rocchio's and Relevance Feedback Method
9.4.3.3 Other Methods
9.5 Applicability of Recommender System in Healthcare and Agriculture
9.5.1 Recommendation System in Healthcare
9.5.2 Recommender System in Agriculture
9.6 Pros and Cons of Content-Based Recommender System
9.7 Conclusion
References
Chapter 10 Content (Item)-Based Recommendation System
10.1 Introduction
10.2 Phases of Content-Based Recommendation Generation
10.3 Content-Based Recommendation Using CosineSimilarity
10.4 Content-Based Recommendations Using Optimization Techniques
10.5 Content-Based Recommendation Using the Tree Induction Algorithm
10.6 Summary
References
Chapter 11 Content-Based Health Recommender Systems
11.1 Introduction
11.2 Typical Health Recommender System Framework
11.3 Components of Content-Based Health Recommender System
11.4 Unstructured Data Processing
11.5 Unsupervised Feature Extraction & Weighting
11.5.1 Bag of Words (BoW)
11.5.2 Word to Vector (Word2Vec)
11.5.3 Global Vectors for Word Representations (Glove)
11.6 Supervised Feature Selection & Weighting
11.7 Feedback Collection
11.7.1 Medication & Therapy
11.7.2 Healthy Diet Plan
11.7.3 Suggestions
11.8 Training & Health Recommendation Generation
11.8.1 Analogy-Based ML in CBHRS
11.8.2 Specimen-Based ML in CBHRS
11.9 Evaluation of Content-Based Health Recommender System
11.10 Design Criteria of CBHRS
11.10.1 Micro-Level & Lucidity
11.10.2 Interactive Interface
11.10.3 Data Protection
11.10.4 Risk & Uncertainty Management
11.10.5 Doctor-in-Loop (DiL)
11.11 Conclusions and Future Research Directions
References
Chapter 12 Context-Based Social Media Recommendation System
12.1 Introduction
12.2 Literature Survey
12.3 Motivation and Objectives
12.3.1 Architecture
12.3.2 Modules
12.3.3 Implementation Details
12.4 Performance Measures
12.5 Precision
12.6 Recall
12.7 FMeasure
12.8 Evaluation Results
12.9 Conclusion and Future Work
References
Chapter 13 Netflix Challenge-Improving Movie Recommendations
13.1 Introduction
13.2 Data Preprocessing
13.3 MovieLens Data
13.4 Data Exploration
13.5 Distributions
13.6 Data Analysis
13.7 Results
13.8 Conclusion
References
Chapter 14 Product or Item-Based Recommender System
14.1 Introduction
14.2 Various Techniques to Design Food Recommendation System
14.2.1 Collaborative Filtering Recommender Systems
14.2.2 Content-Based Recommender Systems (CB)
14.2.3 Knowledge-Based Recommender Systems
14.2.4 Hybrid Recommender Systems
14.2.5 Context Aware Approaches
14.2.6 Group-Based Methods
14.2.7 Different Types of Food Recommender Systems
14.3 Implementation of Food Recommender System Using Content-Based Approach
14.3.1 Item Profile Representation
14.3.2 Information Retrieval
14.3.3 Word2vec
14.3.4 How are word2vec Embedding's Obtained?
14.3.5 Obtaining word2vec Embeddings
14.3.6 Dataset
14.3.6.1 Data Preprocessing
14.3.7 Web Scrapping For Food List
14.3.7.1 Porter Stemming All Words
14.3.7.2 Filtering Our Ingredients
14.3.7.3 Final Data Frame with Dishes and Their Ingredients
14.3.7.4 Hamming Distance
14.3.7.5 Jaccard Distance
14.4 Results
14.5 Observations
14.6 Future Perspective of Recommender Systems
14.6.1 User Information Challenges
14.6.1.1 User Nutrition Information Uncertainty
14.6.1.2 User Rating Data Collection
14.6.2 Recommendation Algorithms Challenges
14.6.2.1 User Information Such as Likes/Dislikes Food or Nutritional Needs
14.6.2.2 Recipe Databases
14.6.2.3 A Set of Constraints or Rules
14.6.3 Challenges Concerning Changing Eating Behavior of Consumers
14.6.4 Challenges Regarding Explanations and Visualizations
14.7 Conclusion
Acknowledgements
References
Part 4 Blockchain & IoT-Based Recommender Systems
Chapter 15 A Trust-Based Recommender System Built on IoT Blockchain Network With Cognitive Framework
15.1 Introduction
15.1.1 Today and Tomorrow
15.1.2 Vision
15.1.3 Internet of Things
15.1.4 Blockchain
15.1.5 Cognitive Systems
15.1.6 Application
15.2 Technologies and its Combinations
15.2.1 IoT-Blockchain
15.2.2 IoT-Cognitive System
15.2.3 Blockchain-Cognitive System
15.2.4 IoT-Blockchain-Cognitive System
15.3 Crypto Currencies With IoT-Case Studies
15.4 Trust-Based Recommender System
15.4.1 Requirement
15.4.2 Things Management
15.4.3 Cognitive Process
15.5 Recommender System Platform
15.6 Conclusion and Future Directions
References
Chapter 16 Development of a Recommender System HealthMudra Using Blockchain for Prevention of Diabetes
16.1 Introduction
16.2 Architecture of Blockchain
16.2.1 Definition of Blockchain
16.2.2 Structure of Blockchain
16.3 Role of HealthMudra in Diabetic
16.4 Blockchain Technology Solutions
16.4.1 Predictive Models of Health Data Analysis
16.5 Conclusions
References
Part 5 Healthcare Recommender Systems
Chapter 17 Case Study 1: Health Care Recommender Systems
17.1 Introduction
17.1.1 Health Care Recommender System
17.1.2 Parkinson's Disease: Causes and Symptoms
17.1.3 Parkinson's Disease: Treatment and Surgical Approaches
17.2 Review of Literature
17.2.1 Machine Learning Algorithms for Parkinson's Data
17.2.2 Visualization
17.3 Recommender System for Parkinson's Disease (PD)
17.3.1 How Will One Know When Parkinson's has Progressed?
17.3.2 Dataset for Parkinson's Disease (PD)
17.3.3 Feature Selection
17.3.4 Classification
17.3.4.1 Logistic Regression
17.3.4.2 K Nearest Neighbor (KNN)
17.3.4.3 Support Vector Machine (SVM)
17.3.4.4 Decision Tree
17.3.5 Train and Test Data
17.3.6 Recommender System
17.4 Future Perspectives
17.5 Conclusions
References
Chapter 18 Temporal Change Analysis-Based Recommender System for Alzheimer Disease Classification
18.1 Introduction
18.2 Related Work
18.3 Mechanism of TCA-RS-AD
18.4 Experimental Dataset
18.5 Neural Network
18.6 Conclusion
References
Chapter 19 Regularization of Graphs: Sentiment Classification
19.1 Introduction
19.2 Neural Structured Learning
19.3 Some Neural Network Models
19.4 Experimental Results
19.4.1 Base Model
19.4.2 Graph Regularization
19.5 Conclusion
References
Chapter 20 TSARS: A Tree-Similarity Algorithm-Based Agricultural Recommender System
20.1 Introduction
20.2 Literature Survey
20.3 Research Gap
20.4 Problem Definitions
20.5 Methodology
20.6 Results & Discussion
20.6.1 Performance Evaluation
20.6.2 Time Complexity Analysis
20.7 Conclusion & Future Work
References
Chapter 21 Influenceable Targets Recommendation Analyzing Social Activities in Egocentric Online Social Networks
21.1 Introduction
21.2 Literature Review
21.3 Dataset Collection Process with Details
21.3.1 Main User's Activities Data
21.3.2 Network Member's Activities Data
21.3.3 Tools and Libraries for Data Collection
21.3.4 Details of the Datasets
21.4 Primary Preprocessing of Data
21.4.1 Language Detection and Translation
21.4.2 Tagged Tweeters Collection
21.4.3 Textual Noise Removal
21.4.4 Textual Spelling Correction
21.5 Influence and Social Activities Analysis
21.5.1 Step 1: Targets Selection From OSMs
21.5.2 Step 3: Categories Classification of Social Contents
21.5.3 Step 4: Sentiments Analysis of Social Contents
21.6 Recommendation System
21.6.1 Secondary Preprocessing of Data
21.6.2 Recommendation Analyzing Contents of Social Activities
21.7 Top Most Influenceable Targets Evaluation
21.8 Conclusion
21.9 Future Scope
References
Index
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