Recommender System with Machine Learning and Artificial Intelligence 1st edition by Priya Gupta, Sachi Nadan Mohanty, Sarika Jain ISBN 1119711606 9781119711605

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ISBN 10: 1119711606
ISBN 13: 9781119711605
Author: Priya Gupta, Sachi Nadan Mohanty, Sarika Jain

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.

Recommender System with Machine Learning and Artificial Intelligence 1st Table of contents:

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

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Tags: Priya Gupta, Sachi Nadan Mohanty, Sarika Jain, Recommender System, Machine Learning, Artificial Intelligence