Unified probabilistic matrix factorization of follow

XIONG Xiao-quan; ZHANG Ming-xin

Chinese

您当前的位置：

首页 >

文章列表页 >

Unified probabilistic matrix factorization of follow

更新时间：2026-09-04

- Unified probabilistic matrix factorization of follow
- Vol. 36, Issue 8, Pages: 92-96(2019)
- 作者机构：
  
  1. 苏州大学计算机科学与技术学院,江苏,苏州,215006
  2. 常熟理工学院机构计算机科学与工程学院, 江苏常熟,215500
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2019
- 稿件说明：
移动端阅览
熊孝全, 张明新. 基于联合概率矩阵分解的微博关注推荐算法[J]. 微电子学与计算机, 2019, 36(8): 92-96.

XIONG Xiao-quan, ZHANG Ming-xin. Unified probabilistic matrix factorization of follow[J]. 2019, 36(8): 92-96.
熊孝全, 张明新. 基于联合概率矩阵分解的微博关注推荐算法[J]. 微电子学与计算机, 2019, 36(8): 92-96. DOI：

XIONG Xiao-quan, ZHANG Ming-xin. Unified probabilistic matrix factorization of follow[J]. 2019, 36(8): 92-96. DOI：

摘要

在传统的概率矩阵分解算法基础上

考虑交互行为的时效性建立用户影响力模型

并结合静态关注关系

提出了基于联合概率矩阵分解的微博关注推荐算法(UPMFF-L).在使用scrapy爬取新浪微博得到的数据集上的实验结果表明

与NMF、PMF和SoRec算法相比

UPMFF-L算法在不同数据密度下的F1-Measure平均提升了11.82%.

Abstract

We add the timeliness of interaction behavior into the traditional probability matrix factorization algorithm to establish the newly proposed user influence model Combined with the static attention relationship

a recommendation algorithm based on unified probabilistic matrix factorization is proposed. The experimental results on the data set established by Sina Weibo show that compared with NMF

PMF

SoRec and other algorithms

the proposed algorithm has an average increase of 11.82% on the F1-Measure index under different data densities.

关键词

Keywords

references

陈婷, 朱青, 周梦溪, 等.社交网络环境下基于信任的推荐算法[J].软件学报, 2017, 28(3):721-731. DOI: 721

孙光福, 吴乐, 刘淇, 等.基于时序行为的协同过滤推荐算法[J].软件学报, 2013, 24(11):2721-2733. DOI: 2721

LEE D D, SEUNG H S. Algorithms for non-negative matrix factorization[C]//Advances in neural information processing systems. 2001: 556-562.

MA H, YANG H, LYU M R, et al. SoRec: social recommendation using probabilistic matrix factorization[C]// ACM, 2008: 931-940.

徐志明, 李栋, 刘挺, 等.微博用户的相似性度量及其应用[J].计算机学报, 2014, 37(1):207-218. DOI: 207

TANG J, WANG S, HU X, et al. Recommendation with Social Dimensions[C]// Thirtieth Aaai Conference on Artificial Intelligence. AAAI Press, 2016.

郭磊, 马军, 陈竹敏, 等.一种结合推荐对象间关联关系的社会化推荐算法[J].计算机学报, 2014, 37(1):219-228. DOI: 219

MNIH A, SALAKHUTDINOV R R. Probabilistic matrix factorization[C]//Advances in neural information processing systems. 2008: 1257-1264.

穆俊.基于关联规则挖掘的社区网络数据爬虫算法[J].微电子学与计算机, 2018, 35(8):105-108. DOI: 105

YU Y, MO L, ZHOU J. Social Friend Interest Similarity in Microblog and Its Implication[J]. International Journal of Control % Automation, 2015, 8(11):21-32. DOI: 21

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Aerial small object detection algorithm based on improved RT-DETR

High gain stability high temperature operational amplifier based on reverse body bias

Simulation on the DC characteristics of double heterojunction p-GaN HEMT with AlN/GaN hybrid buffer layer

A high-efficiency charge pump for ultra-low voltage boost systems

Lightweight countermeasures against Spectre attacks on the branch prediction unit

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰