A Course Recommender System of MOOC Based on Collaborative Filtering Algorithm with Improved Pearson Correlation Coefficient

目录

声明

Abstract

Table of contents

List of figures

List of tables

Chapter 1 Introduction

1．1 Research Background

1．2 Overseas and Domestic Research Status

1．3 Our Contribution

Chapter 2 Literatire Keview on Recommendation Algorithms

2．1 Related Work of Recommendation Algorithms

2．1．1 Item-based Collaborative Filtering

2．1．2 User-based Collaborative Filtering

2．2 Performance Evaluation Indexes of Recommendation Algorithm

2．2．1 Prediction Accuracy Rate of Recommendation

2．2．2 Prediction Coverage Rate of Recommendation

2．2．3 Diversity of Recommendation

2．3 Summary of Existing Methods

2．4 Summary

Chapter 3 A User-based Collaborative Filtering Algorithm with Improved PCC

3．1 The Procedure of User-based Collaborative Filtering with Improved PCC

3．1．1 Overview of User-CF Algorithm with Improved PCC

3．1．2 Improved Pearson Correlation Coefficient

3．1．3 Neighbor Set Construction Based on User Similarity

3．1．4 Rating Prediction Based on Neighbor Set

3．2．1 Dataset Preparation for Experiment

3．2．2 Experimental Design

3．2．3 Experimental Results

3．3 Summary

Chapter 4 Course Recommendation Based on Mixed Similarity with Improved PCC

4．1 Mixed Similarity Calculation with Multipliers of Improved PCC

4．1．1 Calculation of User Mixed Similarity

4．1．2 Similarity Optimization with Multipliers of Improved PCC

4．1．3 Recommendation Rating Correction Module Based on Quality Index

4．2 Experimental Results for Personalized Recommendation

4．2．1 Experimental Design

4．2．2 Experimental Results

4．3 Summary

Chapter 5 Summary and Future Work

5．1 Summary

5．2 Future Work

References

Acknowledgements

Appendix