基于矩阵分解的社会化推荐模型

严素蓉, 冯小青, 廖一星

清华大学学报（自然科学版） ›› 2016, Vol. 56 ›› Issue (7) : 793-800.

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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

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
作者信息 +

Matrix factorization based social recommender model

  • YAN Surong1,2, FENG Xiaoqing1, LIAO Yixing1
Author information +
文章历史 +

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。

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.

关键词

大数据 / 社交网络 / 矩阵分解 / 稀疏性 / 可扩展性

Key words

big data / social networks / matrix factorization / sparsity / scalability

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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 https://doi.org/10.16511/j.cnki.qhdxxb.2016.21.045
YAN Surong, FENG Xiaoqing, LIAO Yixing. Matrix factorization based social recommender model[J]. Journal of Tsinghua University(Science and Technology). 2016, 56(7): 793-800 https://doi.org/10.16511/j.cnki.qhdxxb.2016.21.045
中图分类号： TP391.6   

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基金

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）

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