A microblog recommendation algorithm based on social tagging and a temporal interest evolution model*

Yuan Zhen-ming; Huang Chi; Sun Xiao-yan; Li Xing-xing; Xu Dong-rong

doi:10.1631/FITEE.1400368

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A microblog recommendation algorithm based on social tagging and a temporal interest evolution model*

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- A microblog recommendation algorithm based on social tagging and a temporal interest evolution model*
- 基于社会标签和时间兴趣演变模型的微博推荐算法
- Frontiers of Information Technology & Electronic Engineering Vol. 16, Issue 7, Pages: 532-540(2015)
- Affiliations：
  
  1.School of Information Science and Engineering, Hangzhou Normal University, Hangzhou 311121, China
  2.MRI Unit & Epidemiology Division, Psychiatry Department, Columbia University & New York State Psychiatric Institute, New York 10032, USA
- Author bio：
  
  ‡Corresponding Author
- Funds：
- DOI：10.1631/FITEE.1400368
  CLC：
- Received：30 October 2014，
  
  Revised：2015-06-08，
  
  Accepted：08 April 2015，
  
  Published：2015-07
- Accepted：
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Yuan Zhen-ming, Huang Chi, Sun Xiao-yan, et al. A microblog recommendation algorithm based on social tagging and a temporal interest evolution model*[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(7): 532-540.
DOI：

Yuan Zhen-ming, Huang Chi, Sun Xiao-yan, et al. A microblog recommendation algorithm based on social tagging and a temporal interest evolution model*[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(7): 532-540. DOI： 10.1631/FITEE.1400368.

摘要

目的

微博推荐面临用户冷启动和主题兴趣变化的挑战。研究考虑主题兴趣变化的个性化微博推荐算法，可在一定程度上同时解决用户冷启动问题。

创新点

提出一种基于时间兴趣演变模型和社会标签预测的协同过滤推荐算法。该算法充分利用社会网络和标签热度随时间的演变模型，提高了推荐准确率。

方法

首先，用三个矩阵建模用户、标签和微博之间的关系（图2）。然后根据标签的兴趣演变模型优化每个微博的标签评分（图3）。对于用户冷启动问题，设计一种基于社区发现和最大标签投票算法来预测用户相关的标签。最后，给用户推荐具有最大候选标签集概率的前n个微博（图1）。

结论

在新浪微博数据集上的实验验证了所提算法在获得好的招回率和准确率的同时，可以较好地符合时间演变性能。问卷调查也证明了在冷启动发生时推荐结果的用户满意度。

Abstract

Personalized microblog recommendations face challenges of user cold-start problems and the interest evolution of topics. In this paper

we propose a collaborative filtering recommendation algorithm based on a temporal interest evolution model and social tag prediction. Three matrices are first prepared to model the relationship between users

关键词

Keywords

references

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