声明
Acknowledgements
Abstract
Table of contents
List of figures
List of tables
Chapter 1 Introduction
1．1 Research Background
1．2 Research Status
1．2．1 Distributed computing platform Spark
1．2．2 Hybrid Algorithm Recommendation System
1．2．3 Hybrid Algorithm Recommendation System Challenges
1．3 Research Content and Paper Structure
1．3．1 Main Research Contents
1．3．2 Paper Structure
Chapter 2 Related Work
2．1 Spark Distributed Computing Framework
2．1．1 The Spark Overview
2．1．2 Spark Design Concept
2．1．3 Resilient Distributed Datasets
2．2 Recommended System
2．2．1 Concept of Recommended System
2．2．2 Overview of Recommended Algorithms
2．2．3 Collaborative Filtering Algorithm
2．2．4 User-based Collaborative Filtering Recommendation
2．2．5 Item-based Collaborative Filtering Recommendation
2．2．6 Content-based Recommendation Algorithm
2．2．7 Model-based Recommendation Algorithm
2．2．8 Recommended System Evaluation Indicators
Chapter 3 Hybrid Recommendation Systems Based on Spark Platform
3．1 Current Status Analysis
3．2 Hybrid Algorithm Recommendation Overall Architecture
3．3 Data Module
3．4 Algorithm Module
3．4．1 Algorithm Module Design
3．4．2 Mixed Weight Calculation Method(MWCM)Design
3．5 Recommended Module
3．6．1 User-based Collaborative Filtering Algorithm
3．6．2 Item-based Collaborative Filtering Algorithm
3．6．3 Latent Factor Modle Algorithm
3．6．4 Hybrid Recommendation Algorithmn
Chapter 4 Experimental Evaluation
4．1 Evaluation Indicators
4．2 Recommended Algorithm Accuracy Test
4．2．1 Impact of Data Size on Recommendation Accuracy
4．2．2 Impact of Different Data Sets on Recommendation Accuracy
4．2．3 Effect of Recommendation Algorithm on Recommendation Accuracy
4．3 Distributed Framework Efficiency Test
4．3．1 Spark Framework Efficiency Test
4．4 Spark System Scalability Test
4．5 Conclusion
Chapter 5 Summary and Future Work
5．1 Summary
5．2 Future Work
References
Appendix A
