Gibbs Sampling Inference Based RTM Model for Micro-blog Personalized Label Recommendation

LIU Zhen-zhen; XU Dong-ping

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Gibbs Sampling Inference Based RTM Model for Micro-blog Personalized Label Recommendation

更新时间：2026-09-04

- Gibbs Sampling Inference Based RTM Model for Micro-blog Personalized Label Recommendation
- Vol. 34, Issue 12, Pages: 138-144(2017)
- 作者机构：
  
  武汉理工大学计算机科学与技术学院,湖北,武汉,430070
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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刘真臻, 徐东平. 微博个性化标签图形化RTM模型Gibbs采样推荐[J]. 微电子学与计算机, 2017, 34(12): 138-144.

LIU Zhen-zhen, XU Dong-ping. Gibbs Sampling Inference Based RTM Model for Micro-blog Personalized Label Recommendation[J]. 2017, 34(12): 138-144.
刘真臻, 徐东平. 微博个性化标签图形化RTM模型Gibbs采样推荐[J]. 微电子学与计算机, 2017, 34(12): 138-144. DOI：

LIU Zhen-zhen, XU Dong-ping. Gibbs Sampling Inference Based RTM Model for Micro-blog Personalized Label Recommendation[J]. 2017, 34(12): 138-144. DOI：

摘要

为提高个性化标签推荐方法性能

提出基于Gibbs采样推理的微博个性化标签隐含关系主题模型(Relation Topic Model

RTM)推荐算法.首先

利用图形化形式对微博中的潜在局部信息进行表达

对用户主题分布为代表的用户进行top-k相似用户发现

然后计算出现在这些用户中的所有标签的频率

并推荐与用户最相关的标签.其次

为挖掘潜在主题信息

利用带惩罚项的增强型余弦相似度RTM模型对微博标签进行命名

大大提高联合建模对潜在主题生成标签的影响

并可发现全局标签和主题之间的关系; 最后

通过真实的实验结果显示

所提推荐方法要优于选取的TF-IDF、RTMSA等几种经典标签推荐算法

验证了算法有效性.

Abstract

In order to improve the performance of personalized tag recommendation method

the Gibbs sampling inference based RTM model for Micro-blog personalized label recommendation was proposed. Firstly

we used the user topic distribution as the representative of the user with Top-k similar for the potential local information in micro-blog

Then we calculated the frequency of all tags appearing in these users

and recommended the most relevant tags with the user. Secondly

in order to explore the potential theme information

we named the micro-blog tags by using the implicit relation model

which could greatly improves the influence of the joint modeling on the latent topic generation tags

and finds out the relationship between the global tags and the topic; Finally

the experimental results show that the proposed method is superior to the selected TF-IDF

RTMSA and other classical label recommendation algorithm

and verify the effectiveness of the proposed algorithm.

关键词

Keywords

references

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