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数据分析与知识发现  2017, Vol. 1 Issue (4): 76-83     https://doi.org/10.11925/infotech.2096-3467.2017.04.09
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于图像语义的用户兴趣建模*
曾金1,3, 陆伟1,2(), 丁恒1, 陈海华1
1武汉大学信息管理学院 武汉 430072
2武汉大学信息检索与知识挖掘研究所 武汉 430072
3武汉传媒学院文化管理学院 武汉 430205
Modeling User’s Interests Based on Image Semantics
Zeng Jin1,3, Lu Wei1,2(), Ding Heng1, Chen Haihua1
1School of Information Management, Wuhan University, Wuhan 430072, China
2Institute for Information Retrieval and Knowledge Mining, Wuhan University, Wuhan 430072, China
3School of Culture Management, Wuhan College of Media and Communications, Wuhan 430072, China
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摘要 

目的】社交网络环境下的用户兴趣建模是好友推荐、精准营销的关键, 利用微博用户分享的图像, 提出一种基于图像语义的用户兴趣建模方法, 旨在更加准确地预测用户的真实兴趣。【方法】在获取新浪微博用户图像数据的基础上, 使用图像的高层语义表达用户兴趣特征, 基于这些特征使用SVM训练得到图像语义分类器进行预测。【结果】实验结果表明, 本文建立的模型能够较为准确地预测用户真实兴趣, 169位用户分类的准确率达到97.38%, 召回率为98.92%, F值为98.14%。【局限】由于实验图像数据集有限, 未能完整地覆盖用户所有的兴趣类别。【结论】该模型能够基于用户分享的图像较为准确地预测用户兴趣, 表明了图像高层语义的有效性, 同时为图像高层语义应用研究提供了一定的理论和技术基础。

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曾金
陆伟
丁恒
陈海华
关键词 图像语义用户兴趣建模社交网络支持向量机    
Abstract

[Objective] This paper aims to predict the user’s interests accurately with a new modeling method based on the semantics of images shared on the microblogs. [Methods] First, we crawled the image data of Sina microblogging users. Then, we used high-level semantic information from these images. Finally, we predicted user’s interests based on the image semantic classifier by the SVM training. [Results] The proposed method could predict user’s interests effectively. Among the 169 Sina microblogging users, the precision, recall and F-values were 97.38%, 98.92% and 98.14%, respectively. [Limitations] The size of the test corpus needs to be expanded to have more comprehensive results. [Conclusions] The proposed model could predict user’s interests effectively, which lays some theoretical and technical foundations for the application of high-level image semantics.

Key wordsImage Semantic    User Interest Modeling    Social Network    Support Vector Machine
收稿日期: 2017-01-12      出版日期: 2017-05-24
ZTFLH:  G353  
基金资助:*本文系国家自然科学基金面上项目“面向词汇功能的学术文本语义识别与知识图谱构建”(项目编号: 71473183)的研究成果之一
引用本文:   
曾金, 陆伟, 丁恒, 陈海华. 基于图像语义的用户兴趣建模*[J]. 数据分析与知识发现, 2017, 1(4): 76-83.
Zeng Jin,Lu Wei,Ding Heng,Chen Haihua. Modeling User’s Interests Based on Image Semantics. Data Analysis and Knowledge Discovery, 2017, 1(4): 76-83.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.04.09      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2017/V1/I4/76
  用户分享图像及用户兴趣标签
  模型无法识别图像
用户类别 用户总计 图像总计
旅游 42 12 530
时尚 40 11 901
动漫 37 10 751
模特 30 8 833
美食 20 5 900
总数 169 49 915
  5个类别用户和图像数目
  特征值
兴趣类型 P准确率 R召回率 F值
旅游 100% 100% 100%
时尚 95.56% 100% 97.73%
动漫 94.59% 94.59% 94.59%
模特 96.77% 100% 98.36%
美食 100% 100% 100%
微平均 97.17% 98.81% 97.98%
宏平均 97.38% 98.92% 98.14%
  用户兴趣分类识别效果
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