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Collaborative Filtering Using Data Mining and Analysis

Book Description

Internet usage has become a normal and essential aspect of everyday life. Due to the immense amount of information available on the web, it has become obligatory to find ways to sift through and categorize the overload of data while removing redundant material. Collaborative Filtering Using Data Mining and Analysis evaluates the latest patterns and trending topics in the utilization of data mining tools and filtering practices. Featuring emergent research and optimization techniques in the areas of opinion mining, text mining, and sentiment analysis, as well as their various applications, this book is an essential reference source for researchers and engineers interested in collaborative filtering.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Book Series
    1. Mission
    2. Coverage
  5. Editorial Advisory Board and List of Reviewers
    1. Editorial Advisory Board
  6. Foreword
  7. Preface
    1. SECTION 1: DATA MINING TECHNIQUES AND ANALYSIS: AN OVERVIEW
    2. SECTION 2: COLLABORATIVE FILTERING: AN INTRODUCTION
    3. SECTION 3: APPLICATIONS OF DATA MINING TECHNIQUES AND DATA ANALYSIS IN COLLABORATIVE FILTERING
  8. Acknowledgment
  9. Section 1: Data Mining Techniques and Analysis: An Overview
    1. Chapter 1: Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System
      1. ABSTRACT
      2. INTRODUCTION: DATA MINING, RECOMMENDATION SYSTEMS AND PERSONALIZED ADAPTIVE E-LEARNING SYSTEMS
      3. RESEARCH GAPS
      4. SUMMARY
      5. REFERENCES
    2. Chapter 2: Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MODIFIED SINGLE PASS CLUSTERING ALGORITHM
      5. PERFORMANCE EVALUATION OF MSPC ALGORITHM BASED ON MEDIAN AS A THRESHOLD VALUE
      6. CHAPTER SUMMARY
      7. REFERENCES
    3. Chapter 3: Dimensionality Reduction Techniques for Text Mining
      1. ABSTRACT
      2. INDRODUCTION
      3. SENTIMENT CLASSIFICATION
      4. BACKGROUND
      5. PROPOSED METHODOLOGY
      6. TRAINING MODEL AND EVALUATION
      7. PREDICTION
      8. EXPERIMENTS AND RESULTS
      9. CONCLUSION AND FUTURE SCOPE
      10. REFERENCES
      11. KEY TERMS AND DEFINITIONS
  10. Section 2: Collaborative Filtering: An Introduction
    1. Chapter 4: History and Overview of the Recommender Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. CLASSIFICATION METHOD
      4. A BRIEF HISTORY / OVERVIEW OF RECOMMENDER SYSTEMS
      5. CLASSIFICATION
      6. TECHNIQUES APPLIED IN RECOMMENDER SYSTEMS
      7. CLASSIFICATION TECHNIQUES
      8. CLUSTERING
      9. FILTERING TECHNIQUES
      10. SIMILARITY-BASED RECOMMENDER SYSTEMS
      11. EVALUATING RECOMMENDER SYSTEMS
      12. AREA OF APPLICATIONS
      13. ISSUES IN RECOMMENDER SYSTEMS
      14. CONCLUSION
      15. REFERENCES
    2. Chapter 5: A Classification Framework Towards Application of Data Mining in Collaborative Filtering
      1. ABSTRACT
      2. INTRODUCTION
      3. RESEARCH METHODOLOGY
      4. INTRODUCTION TO COLLABORATIVE FILTERING
      5. INTRODUCTION TO DATA MINING
      6. CLASSIFICATION FRAMEWORK
      7. HYPOTHETICAL CASE STUDY
      8. IMPLICATIONS
      9. CONCLUSION AND FUTURE SCOPE
      10. REFERENCES
      11. ADDITIONAL READING
      12. KEY TERMS AND DEFINITIONS
    3. Chapter 6: Collaborative Filtering Based Data Mining for Large Data
      1. ABSTRACT
      2. INTRODUCTION
      3. COLLABORATIVE FILTERING
      4. ITEM-BASED COLLABORATIVE FILTERING
      5. MODEL BASED COLLABORATIVE FILTERING
      6. CLUSTER-BASED COLLABORATIVE FILTERING APPROACH
      7. A FEATURE BASED MODEL
      8. MAPREDUCE BASED COLLABORATIVE FILTERING
      9. CONCLUSION
      10. REFERENCES
      11. ADDITIONAL READING
      12. KEY TERMS AND DEFINITIONS
    4. Chapter 7: Big Data Mining Using Collaborative Filtering
      1. ABSTRACT
      2. INTRODUCTION
      3. CONCLUSION
      4. REFERENCES
      5. ADDITIONAL READING
      6. KEY TERMS AND DEFINITIONS
  11. Section 3: Applications of Data Mining Techniques and Data Analysis in Collaborative Filtering
    1. Chapter 8: Collaborative and Clustering Based Strategy in Big Data
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE SURVEY
      4. AN INTRODUCTION TO COLLABORATIVE AND CLUSTERING BASED STRATEGY IN BIG DATA
      5. BIG DATA CLUSTERING
      6. BIG DATA CLUSTERING EXAMPLES
      7. FUTURE CASES OF BIG DATA CLUSTERING
      8. BIG DATA COLLABORATION
      9. EXAMPLE OF COLLABORATIVE FILTERING AND CLUSTERING FILTERING IN BIG DATA
      10. CONCLUSION
      11. REFERENCES
      12. KEY TERMS AND DEFINITIONS
    2. Chapter 9: Association Rule Mining in Collaborative Filtering
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. CF-MINER: ASSOCIATION RULE MINING FOR COLLABORATIVE FILTERING
      5. EVALUATION
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
    3. Chapter 10: A Classification Framework on Opinion Mining for Effective Recommendation Systems
      1. ABSTRACT
      2. INTRODUCTION
      3. LITERATURE REVIEW
      4. RESEARCH DESIGN
      5. INFERENCE OF RESEARCH
      6. LIMITATION
      7. CONCLUSION AND FUTURE RESEARCH
      8. REFERNCES
      9. ADDITIONAL READING
      10. KEY TERMS AND DEFINITIONS
    4. Chapter 11: Combining User Co-Ratings and Social Trust for Collaborative Recommendation
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND AND RELATED WORKS
      4. COMBINING USER SIMILARITY AND SOCIAL-TRUST FOR CF RECOMMENDATION
      5. EXPERIMENTS AND RESULTS
      6. CONCLUSION
      7. REFERENCES
      8. KEY TERMS AND DEFINITIONS
    5. Chapter 12: Visual Data Mining for Collaborative Filtering
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. CONCLUSION
      5. REFERENCES
      6. KEY TERMS AND DEFINITIONS
    6. Chapter 13: Data Stream Mining Using Ensemble Classifier
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. MAIN FOCUS OF THE CHAPTER
      5. REAL TIME APPLICATIONS
      6. FUTURE RESEARCH DIRECTIONS
      7. CONCLUSION
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
    7. Chapter 14: Statistical Relational Learning for Collaborative Filtering a State-of-the-Art Review
      1. ABSTRACT
      2. INTRODUCTION
      3. BACKGROUND
      4. RECOMMENDER SYSTEMS
      5. STATISTICAL RELATIONAL LEARNING FOR COLLABORATIVE FILTERING
      6. SUMMARY
      7. CONCLUSION AND FURTHER WORK
      8. REFERENCES
      9. KEY TERMS AND DEFINITIONS
  12. Compilation of References
  13. About the Contributors