A Constrained Trust Recommendation Using Probabilistic Matrix Factorization

YIN Gui-sheng; ZHANG Ya-nan; DONG Yu-xin; HAN Qi-long

doi:10.3969/j.issn.0372-2112.2014.05.011

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A Constrained Trust Recommendation Using Probabilistic Matrix Factorization

更新时间：2025-07-16

- A Constrained Trust Recommendation Using Probabilistic Matrix Factorization
- Acta Electronica Sinica Vol. 42, Issue 5, Pages: 904-911(2014)
- 作者机构：
  
  哈尔滨工程大学计算机科学与技术学院,黑龙江,哈尔滨,150001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.612721836, No.61100007);Post-doctoral Foundation of Heilongjiang Province (No.LBH-Z12068);Free Exploration Foundation of Harbin Engineering University (No.HEUCF100608)
- DOI：10.3969/j.issn.0372-2112.2014.05.011
  CLC： TP391
- Published：2014
- 稿件说明：
移动端阅览
YIN Gui-sheng, ZHANG Ya-nan, DONG Yu-xin, et al. A Constrained Trust Recommendation Using Probabilistic Matrix Factorization[J]. Acta Electronica Sinica, 2014, 42(5): 904-911.
DOI：

YIN Gui-sheng, ZHANG Ya-nan, DONG Yu-xin, et al. A Constrained Trust Recommendation Using Probabilistic Matrix Factorization[J]. Acta Electronica Sinica, 2014, 42(5): 904-911. DOI： 10.3969/j.issn.0372-2112.2014.05.011.

摘要

现有的推荐算法很难对没有任何记录的冷启动用户或者历史记录稀疏的用户给出准确的推荐，即用户的冷启动问题.本文提出一种基于受限信任关系和概率分解矩阵的推荐方法，由不信任关系约束信任关系的传播，得到准确且覆盖全面的用户信任关系矩阵，并通过对用户信任关系矩阵和用户商品矩阵的概率分解联合用户信任关系和用户商品矩阵信息，为用户给出推荐.实验表明该方法对冷启动用户和历史记录稀疏的用户的推荐效果有较大幅度的提升，有效地解决了用户的冷启动问题.

Abstract

Existing recommendation algorithms can not give accurate recommendations for users who have few historical records or even none

namely the user cold recommendation problem.In this paper

a constrained trust recommendation using probabilistic matrix factorization (CTRPMF) is proposed.The trust is propagated with the constraint of distrust to get accurate and comprehensive trust relationship matrix.User trust relationship matrix and user-item matrix are factorized using probabilistic matrix factorization to mix the information from trust relationship and user-item matrix

in order to give recommendations.The experimental results showed that CTRPMF could greatly improve the effectiveness of recommend ations for cold start users and users with sparse historical data

and effectively solve the cold recommendation problem.

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