Home Page Icon
Home Page
Table of Contents for
Part 2: MACHINE LEARNING-BASED RECOMMENDER SYSTEMS
Close
Part 2: MACHINE LEARNING-BASED RECOMMENDER SYSTEMS
by Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Sarika
Recommender System with Machine Learning and Artificial Intelligence
Cover
Preface
Acknowledgment
Part I: INTRODUCTION TO RECOMMENDER SYSTEMS
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
1.5 Item-Based Recommendation vs. User-Based Recommendation System
1.6 Evaluation Metrics for Recommendation Engines
1.7 Problems with Recommendation Systems and Possible Solutions
1.8 Applications of Recommender Systems
References
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
2.3 Related Work
2.4 Types of Explanation
2.5 Explanation Methodology
2.6 Proposed Theoretical Framework for Explanation-Based Recommender System in Health-Care Domain
2.7 Flowchart
2.8 Conclusion
References
3 2Es of TIS: A Review of Information Exchange and Extraction in Tourism Information Systems
3.1 Introduction
3.2 Information Exchange
3.3 Information Extraction
3.4 Sentiment Annotation
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
4 Concepts of Recommendation System from the Perspective of Machine Learning
4.1 Introduction
4.2 Entities of Recommendation System
4.3 Techniques of Recommendation
4.4 Performance Evaluation
4.5 Challenges
4.6 Applications
4.7 Conclusion
References
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
6 Accuracy-Assured Privacy-Preserving Recommender System Using Hybrid-Based 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
7 Machine Learning-Based Recommender System for Breast Cancer Prognosis
7.1 Introduction
7.2 Related Works
7.3 Methodology
7.4 Results and Discussion
7.5 Conclusion
Acknowledgment
References
8 A Recommended System for Crop Disease Detection and Yield Prediction Using Machine Learning Approach
8.1 Introduction
8.2 Machine Learning
8.3 Recommender System
8.4 Crop Management
8.5 Application—Crop Disease Detection and Yield Prediction
References
Part 3: CONTENT-BASED RECOMMENDER SYSTEMS
9 Content-Based Recommender Systems
9.1 Introduction
9.2 Literature Review
9.3 Recommendation Process
9.4 Techniques Used for Item Representation and Learning User Profile
9.5 Applicability of Recommender System in Healthcare and Agriculture
9.6 Pros and Cons of Content-Based Recommender System
9.7 Conclusion
References
10 Content (Item)-Based Recommendation System
10.1 Introduction
10.2 Phases of Content-Based Recommendation Generation
10.3 Content-Based Recommendation Using Cosine Similarity
10.4 Content-Based Recommendations Using Optimization Techniques
10.5 Content-Based Recommendation Using the Tree Induction Algorithm
10.6 Summary
References
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
11.6 Supervised Feature Selection & Weighting
11.7 Feedback Collection
11.8 Training & Health Recommendation Generation
11.9 Evaluation of Content-Based Health Recommender System
11.10 Design Criteria of CBHRS
11.11 Conclusions and Future Research Directions
References
12 Context-Based Social Media Recommendation System
12.1 Introduction
12.2 Literature Survey
12.3 Motivation and Objectives
12.4 Performance Measures
12.5 Precision
12.6 Recall
12.7 F- Measure
12.8 Evaluation Results
12.9 Conclusion and Future Work
References
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
14 Product or Item-Based Recommender System
14.1 Introduction
14.2 Various Techniques to Design Food Recommendation System
14.3 Implementation of Food Recommender System Using Content-Based Approach
14.4 Results
14.5 Observations
14.6 Future Perspective of Recommender Systems
14.7 Conclusion
Acknowledgements
References
Part 4: BLOCKCHAIN & IOT-BASED RECOMMENDER SYSTEMS
15 A Trust-Based Recommender System Built on IoT Blockchain Network With Cognitive Framework
15.1 Introduction
15.2 Technologies and its Combinations
15.3 Crypto Currencies With IoT–Case Studies
15.4 Trust-Based Recommender System
15.5 Recommender System Platform
15.6 Conclusion and Future Directions
References
16 Development of a Recommender System HealthMudra Using Blockchain for Prevention of Diabetes
16.1 Introduction
16.2 Architecture of Blockchain
16.3 Role of HealthMudra in Diabetic
16.4 Blockchain Technology Solutions
16.5 Conclusions
References
Part 5: HEALTHCARE RECOMMENDER SYSTEMS
17 Case Study 1: Health Care Recommender Systems
17.1 Introduction
17.2 Review of Literature
17.3 Recommender System for Parkinson’s Disease (PD)
17.4 Future Perspectives
17.5 Conclusions
References
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
19 Regularization of Graphs: Sentiment Classification
19.1 Introduction
19.2 Neural Structured Learning
19.3 Some Neural Network Models
19.4 Experimental Results
19.5 Conclusion
References
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
20.7 Conclusion & Future Work
References
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
21.4 Primary Preprocessing of Data
21.5 Influence and Social Activities Analysis
21.6 Recommendation System
21.7 Top Most Influenceable Targets Evaluation
21.8 Conclusion
21.9 Future Scope
References
Index
End User License Agreement
Search in book...
Toggle Font Controls
Playlists
Add To
Create new playlist
Name your new playlist
Playlist description (optional)
Cancel
Create playlist
Sign In
Email address
Password
Forgot Password?
Create account
Login
or
Continue with Facebook
Continue with Google
Sign Up
Full Name
Email address
Confirm Email Address
Password
Login
Create account
or
Continue with Facebook
Continue with Google
Prev
Previous Chapter
3 2Es of TIS: A Review of Information Exchange and Extraction in Tourism Information Systems
Next
Next Chapter
4 Concepts of Recommendation System from the Perspective of Machine Learning
Part 2
MACHINE LEARNING-BASED RECOMMENDER SYSTEMS
Add Highlight
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click
here
login for view all page.
Day Mode
Cloud Mode
Night Mode
Reset