Recommender System with Machine Learning and Artificial Intelligence

Recommender System with Machine Learning and Artificial Intelligence

by Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Sarika Jain, Ahmed A. Elngar, Priya Gupta
Released July 2020
Publisher(s): Wiley-Scrivener
ISBN: 9781119711575

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Book description

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.

Table of contents

  1. Cover
  2. Preface
  3. Acknowledgment
  4. Part I: INTRODUCTION TO RECOMMENDER SYSTEMS
    1. 1 An Introduction to Basic Concepts on Recommender Systems
      1. 1.1 Introduction
      2. 1.2 Functions of Recommendation Systems
      3. 1.3 Data and Knowledge Sources
      4. 1.4 Types of Recommendation Systems
      5. 1.5 Item-Based Recommendation vs. User-Based Recommendation System
      6. 1.6 Evaluation Metrics for Recommendation Engines
      7. 1.7 Problems with Recommendation Systems and Possible Solutions
      8. 1.8 Applications of Recommender Systems
      9. References
    2. 2 A Brief Model Overview of Personalized Recommendation to Citizens in the Health-Care Industry
      1. 2.1 Introduction
      2. 2.2 Methods Used in Recommender System
      3. 2.3 Related Work
      4. 2.4 Types of Explanation
      5. 2.5 Explanation Methodology
      6. 2.6 Proposed Theoretical Framework for Explanation-Based Recommender System in Health-Care Domain
      7. 2.7 Flowchart
      8. 2.8 Conclusion
      9. References
    3. 3 2Es of TIS: A Review of Information Exchange and Extraction in Tourism Information Systems
      1. 3.1 Introduction
      2. 3.2 Information Exchange
      3. 3.3 Information Extraction
      4. 3.4 Sentiment Annotation
      5. 3.5 Comparison of Different Annotations Schemes
      6. 3.6 Temporal and Event Extraction
      7. 3.7 TimeML
      8. 3.8 Conclusions
      9. References
  5. Part 2: MACHINE LEARNING-BASED RECOMMENDER SYSTEMS
    1. 4 Concepts of Recommendation System from the Perspective of Machine Learning
      1. 4.1 Introduction
      2. 4.2 Entities of Recommendation System
      3. 4.3 Techniques of Recommendation
      4. 4.4 Performance Evaluation
      5. 4.5 Challenges
      6. 4.6 Applications
      7. 4.7 Conclusion
      8. References
    2. 5 A Machine Learning Approach to Recommend Suitable Crops and Fertilizers for Agriculture
      1. 5.1 Introduction
      2. 5.2 Literature Review
      3. 5.3 Methodology
      4. 5.4 Results and Analysis
      5. 5.5 Conclusion
      6. References
    3. 6 Accuracy-Assured Privacy-Preserving Recommender System Using Hybrid-Based Deep Learning Method
      1. 6.1 Introduction
      2. 6.2 Overview of Recommender System
      3. 6.3 Collaborative Filtering-Based Recommender System
      4. 6.4 Machine Learning Methods Used in Recommender System
      5. 6.5 Proposed RBM Model-Based Movie Recommender System
      6. 6.6 Proposed CRBM Model-Based Movie Recommender System
      7. 6.7 Conclusion and Future Work
      8. References
    4. 7 Machine Learning-Based Recommender System for Breast Cancer Prognosis
      1. 7.1 Introduction
      2. 7.2 Related Works
      3. 7.3 Methodology
      4. 7.4 Results and Discussion
      5. 7.5 Conclusion
      6. Acknowledgment
      7. References
    5. 8 A Recommended System for Crop Disease Detection and Yield Prediction Using Machine Learning Approach
      1. 8.1 Introduction
      2. 8.2 Machine Learning
      3. 8.3 Recommender System
      4. 8.4 Crop Management
      5. 8.5 Application—Crop Disease Detection and Yield Prediction
      6. References
  6. Part 3: CONTENT-BASED RECOMMENDER SYSTEMS
    1. 9 Content-Based Recommender Systems
      1. 9.1 Introduction
      2. 9.2 Literature Review
      3. 9.3 Recommendation Process
      4. 9.4 Techniques Used for Item Representation and Learning User Profile
      5. 9.5 Applicability of Recommender System in Healthcare and Agriculture
      6. 9.6 Pros and Cons of Content-Based Recommender System
      7. 9.7 Conclusion
      8. References
    2. 10 Content (Item)-Based Recommendation System
      1. 10.1 Introduction
      2. 10.2 Phases of Content-Based Recommendation Generation
      3. 10.3 Content-Based Recommendation Using Cosine Similarity
      4. 10.4 Content-Based Recommendations Using Optimization Techniques
      5. 10.5 Content-Based Recommendation Using the Tree Induction Algorithm
      6. 10.6 Summary
      7. References
    3. 11 Content-Based Health Recommender Systems
      1. 11.1 Introduction
      2. 11.2 Typical Health Recommender System Framework
      3. 11.3 Components of Content-Based Health Recommender System
      4. 11.4 Unstructured Data Processing
      5. 11.5 Unsupervised Feature Extraction & Weighting
      6. 11.6 Supervised Feature Selection & Weighting
      7. 11.7 Feedback Collection
      8. 11.8 Training & Health Recommendation Generation
      9. 11.9 Evaluation of Content-Based Health Recommender System
      10. 11.10 Design Criteria of CBHRS
      11. 11.11 Conclusions and Future Research Directions
      12. References
    4. 12 Context-Based Social Media Recommendation System
      1. 12.1 Introduction
      2. 12.2 Literature Survey
      3. 12.3 Motivation and Objectives
      4. 12.4 Performance Measures
      5. 12.5 Precision
      6. 12.6 Recall
      7. 12.7 F- Measure
      8. 12.8 Evaluation Results
      9. 12.9 Conclusion and Future Work
      10. References
    5. 13 Netflix Challenge—Improving Movie Recommendations
      1. 13.1 Introduction
      2. 13.2 Data Preprocessing
      3. 13.3 MovieLens Data
      4. 13.4 Data Exploration
      5. 13.5 Distributions
      6. 13.6 Data Analysis
      7. 13.7 Results
      8. 13.8 Conclusion
      9. References
    6. 14 Product or Item-Based Recommender System
      1. 14.1 Introduction
      2. 14.2 Various Techniques to Design Food Recommendation System
      3. 14.3 Implementation of Food Recommender System Using Content-Based Approach
      4. 14.4 Results
      5. 14.5 Observations
      6. 14.6 Future Perspective of Recommender Systems
      7. 14.7 Conclusion
      8. Acknowledgements
      9. References
  7. Part 4: BLOCKCHAIN & IOT-BASED RECOMMENDER SYSTEMS
    1. 15 A Trust-Based Recommender System Built on IoT Blockchain Network With Cognitive Framework
      1. 15.1 Introduction
      2. 15.2 Technologies and its Combinations
      3. 15.3 Crypto Currencies With IoT–Case Studies
      4. 15.4 Trust-Based Recommender System
      5. 15.5 Recommender System Platform
      6. 15.6 Conclusion and Future Directions
      7. References
    2. 16 Development of a Recommender System HealthMudra Using Blockchain for Prevention of Diabetes
      1. 16.1 Introduction
      2. 16.2 Architecture of Blockchain
      3. 16.3 Role of HealthMudra in Diabetic
      4. 16.4 Blockchain Technology Solutions
      5. 16.5 Conclusions
      6. References
  8. Part 5: HEALTHCARE RECOMMENDER SYSTEMS
    1. 17 Case Study 1: Health Care Recommender Systems
      1. 17.1 Introduction
      2. 17.2 Review of Literature
      3. 17.3 Recommender System for Parkinson’s Disease (PD)
      4. 17.4 Future Perspectives
      5. 17.5 Conclusions
      6. References
    2. 18 Temporal Change Analysis-Based Recommender System for Alzheimer Disease Classification
      1. 18.1 Introduction
      2. 18.2 Related Work
      3. 18.3 Mechanism of TCA-RS-AD
      4. 18.4 Experimental Dataset
      5. 18.5 Neural Network
      6. 18.6 Conclusion
      7. References
    3. 19 Regularization of Graphs: Sentiment Classification
      1. 19.1 Introduction
      2. 19.2 Neural Structured Learning
      3. 19.3 Some Neural Network Models
      4. 19.4 Experimental Results
      5. 19.5 Conclusion
      6. References
    4. 20 TSARS: A Tree-Similarity Algorithm-Based Agricultural Recommender System
      1. 20.1 Introduction
      2. 20.2 Literature Survey
      3. 20.3 Research Gap
      4. 20.4 Problem Definitions
      5. 20.5 Methodology
      6. 20.6 Results & Discussion
      7. 20.7 Conclusion & Future Work
      8. References
    5. 21 Influenceable Targets Recommendation Analyzing Social Activities in Egocentric Online Social Networks
      1. 21.1 Introduction
      2. 21.2 Literature Review
      3. 21.3 Dataset Collection Process with Details
      4. 21.4 Primary Preprocessing of Data
      5. 21.5 Influence and Social Activities Analysis
      6. 21.6 Recommendation System
      7. 21.7 Top Most Influenceable Targets Evaluation
      8. 21.8 Conclusion
      9. 21.9 Future Scope
      10. References
  9. Index
  10. End User License Agreement

Product information

  • Title: Recommender System with Machine Learning and Artificial Intelligence
  • Author(s): Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Sarika Jain, Ahmed A. Elngar, Priya Gupta
  • Release date: July 2020
  • Publisher(s): Wiley-Scrivener
  • ISBN: 9781119711575