Social Trust Recommendation Algorithm Based on Probability M

发布时间：2021-01-20 20:05

　　随着互联网与电子商务的快速发展,网络信息的迅猛增长导致信息过载问题越来越严重。如何快速准确地发现需要的信息成为大数据时代的热点问题之一。推荐系统正是解决这一问题的有效工具,它通过挖掘用户历史行为数据,为每个用户构建精准的偏好模型,并在此基础上主动为用户推荐可能符合其需求的信息。如今推荐系统往往面临着数据稀疏和冷启动问题,本文围绕基于信任和矩阵分解的社会化推荐算法这一主题展开,探讨如何充分挖掘信任关系来帮助用户更好地进行个性化推荐。分别提出了融合社会信任度和正则化约束的推荐算法和基于动态差异性信任的矩阵分解算法。本文的主要工作如下:1.提出一种融合用户社会信任关系和社会正则化的概率矩阵分解算法,该算法不仅考虑到用户社交网络信息对用户评分的影响,同时引入社交信息正则化约束来对用户潜在特征向量进行约束,提高推荐系统的推荐精度。最终得到的SSRec模型通过实验证明模型的推荐准确度较传统算法得到了提升,数据稀疏性问题的得到缓解。2.考虑不同用户在社交网络中受影响程度的差异性,融合社会正则化约束提出DTRec模型。该模型在概率矩阵分解方法框架的基础上,通过对用户-评分矩阵进行矩阵分解,既避免了该矩... 

【文章来源】： 黄沛 华中师范大学

【文章页数】：73 页

【学位级别】：硕士

【文章目录】：
Abstract
Chapter 1 Introduction
    1.1 Research Background and Significance
    1.2 Research Status
    1.3 The Main Contribution and Innovation of thesis
    1.4 Structure of the thesis
Chapter 2 Recommendation System and Related Theoretical Basis
    2.1 Content-based Recommendation Algorithm
    2.2 Recommendation Algorithm based on Collaborative filtering
        2.2.1 Neighborhood-based Recommendation Algorithm
        2.2.2 Recommendation Algorithm based on Matrix Factorization
    2.3 Hybrid Filtering Recommendation Algorithm
    2.4 Common data set
    2.5 Summary of this Chapter
Chapter 3 Social Trust Recommendation combine with Social Regularization
    3.1 Research Motivation
    3.2 Social Trust Recommendation Model combine with Social Regularization
        3.2.1 Probabilistic Matrix Factorization
        3.2.2 Social Network Matrix Factorization
        3.2.3 Social Regularization
        3.2.4 Final Combined Model
    3.3 Experiment and Results Analysis
        3.3.1 Experimental Environment
        3.3.2 Data Sets
        3.3.3 Comparative Experiment
        3.3.4 Setting the Value of the Parameter
        3.3.5 Setting the Value of the Parameter
    3.4 Summary of this Chapter
Chapter 4 Social Recommendation based on Dynamic Difference Trust
    4.1 Research Motivation
    4.2 Social Recommendation Model based on Dynamic Difference Trust
        4.2.1 Probability Matrix Factorization Model Based on Trust Relationship
        4.2.2 Social Trust Based on User Difference Trust
        4.2.3 Final Combined Model
    4.3 Experiment and Analysis
        4.3.1 Experimental Environment and Data Set
        4.3.2 Comparative Experiment
        4.3.3 Influence of Social Regularization Parameter
    4.4 Recommended System
        4.4.1 Demand Analysis
        4.4.2 Functional Design
        4.4.3 System Implementation
    4.5 Summary of this Chapter
Chapter 5 Summary And Outlook
    5.1 Summary of the Methods in this thesis
    5.2 Work Prospects
References
Acknowledgements
Appendix A Abstract（Chinese）



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