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清华大学学报(自然科学版)  2016, Vol. 56 Issue (7): 793-800    DOI: 10.16511/j.cnki.qhdxxb.2016.21.045
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
基于矩阵分解的社会化推荐模型
严素蓉1,2, 冯小青1, 廖一星1
1. 浙江财经大学 信息学院, 杭州 310018;
2. 加州大学尔湾分校 电子工程与计算机科学系, 加利福尼亚 92697-2625
Matrix factorization based social recommender model
YAN Surong1,2, FENG Xiaoqing1, LIAO Yixing1
1. College of Information, Zhejiang University of Finance and Economics, Hangzhou 310018, China;
2. Department of Electrical Engineering & Computer Science, University of California, Irvine, California 92697-2625, USA
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摘要 该文提出一种经由定制关系网络改进的基于矩阵分解的社会化推荐模型来缓解数据稀疏性和冷启动问题,并进一步改善大数据集导致的可扩展性问题。在该模型中,关系网络的社交影响力被建模为矩阵分解模型的用户-物品(user-item)评分倾向,而同质性则被建模为动态正则项。为了获得更好的预测精度和可扩展性,设计了一个关系网络boosting-shrinking算法,在该算法中,基于用户在数据集中的数据密度,自适应地裁减每个用户的关系网络为其定制个性化的关系网络。在稀疏水平不同的不平衡数据集上的实验表明:相比其他的基于矩阵分解的社会化推荐模型,该模型可以显著提高稀疏数据集的预测精度,有效地缓解冷启动问题,并获得较好的可扩展性。
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严素蓉
冯小青
廖一星
关键词 大数据社交网络矩阵分解稀疏性可扩展性    
Abstract:This study describes an improved matrix factorization based social recommender model that uses tailored relationship networks of users as a solution for the sparsity, cold-start and scalability problems in big datasets. The social influence of the relationship networks is targeted as an extra user-item specific bias for the matrix factorization with the uniformity of relationship networks modeled as dynamic social regularization terms in the matrix factorization. A boosting-shrinking algorithm is used for the relationship networks for better prediction accuracy and scalability where the relationships of each user are tailored to generate personalized relationship networks according to the user-specific data density of the user-item rating matrix and the correlation matrix. Tests on unbalanced datasets with different sparsity levels show that this model significantly improves the prediction accuracy for sparse datasets, effectively addresses the cold-start problem, and has better scalability compared to other state-of-the-art matrix factorization based social recommendation models.
Key wordsbig data    social networks    matrix factorization    sparsity    scalability
收稿日期: 2015-09-11      出版日期: 2016-07-22
ZTFLH:  TP391.6  
基金资助:国家自然科学青年基金项目(61502414,61202197);浙江省自然科学青年基金项目(LQ14F010006)
引用本文:   
严素蓉, 冯小青, 廖一星. 基于矩阵分解的社会化推荐模型[J]. 清华大学学报(自然科学版), 2016, 56(7): 793-800.
YAN Surong, FENG Xiaoqing, LIAO Yixing. Matrix factorization based social recommender model. Journal of Tsinghua University(Science and Technology), 2016, 56(7): 793-800.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2016.21.045  或          http://jst.tsinghuajournals.com/CN/Y2016/V56/I7/793
  图1 用户关系网络的boosting-shrinking算法伪代码
  图2 不同α 和λW 对RMSE的影响
  表1 性能比较
  表2 运行时间比较
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