Recommendation System Based on Spark and Hybrid Weight Algor

发布时间：2021-08-31 09:49

　　The development of Internet technology is changing with each passing day.People used to worry about lack of information.Now they face huge amounts of information but it is difficult to get useful information.People’s worry has changed from the lack of information to how to obtain the valuable information they need in the sea of information.The emergence of the recommendation system solves the problem of information overload to some extent.From the initial single algorithm recommendation system t... 

【文章来源】：华中师范大学湖北省 211工程院校 教育部直属院校

【文章页数】：74 页

【学位级别】：硕士

【文章目录】：
Acknowledgements
Abstract
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
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
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 Spark-based Recommendation Algorithm
        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
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
5 Summary and Future Work
    5.1 Summary
    5.2 Future Work
References
Appendix A



本文编号：3374686