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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (4): 76-83    DOI: 10.11925/infotech.2096-3467.2017.04.09
Orginal Article Current Issue | Archive | Adv Search |
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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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     
Received: 12 January 2017      Published: 24 May 2017
ZTFLH:  G353  

Cite this article:

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.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.04.09     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I4/76

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
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%
[1] Zheng L, Cui S, Yue D, et al.User Interest Modeling Based on Browsing Behavior[C]// Proceedings of the 3rd International Conference on Advanced Computer Theory and Engineering. IEEE, 2010.
[2] Bei X, Hai Z.An Angle-Based Interest Model for Text Recommendation[J]. Future Generation Computer Systems, 2016, 64: 211-226.
doi: 10.1016/j.future.2016.04.011
[3] Jung S, Herlocker J L, Webster J.Click Data as Implicit Relevance Feedback in Web Search[J]. Information Processing & Management, 2007, 43(3): 791-807.
[4] Krulwich B.Lifestyle Finder: Intelligent User Profiling Using Large-scale Demographic Data[J]. AI Magazine, 1997, 18(2): 37-45.
[5] Yang C, Zhou Y, Chiu D M.Who are Like-minded: Mining User Interest Similarity in Online Social Networks[OL]. arXiv Preprint, arXiv:1603.02175.
[6] Chen Z H.Modeling Research on Micro-blog Users[C]// Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering. 2013.
[7] Jiang B, Sha Y.Modeling Temporal Dynamics of User Interests in Online Social Networks[J]. Procedia Computer Science, 2015, 51(1): 503-512.
doi: 10.1016/j.procs.2015.05.275
[8] Yin H, Cui B, Chen L, et al.A Temporal Context-aware Model for User Behavior Modeling in Social Media Systems[C]// Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. 2014: 1543-1554.
[9] 邱云飞, 王琳颍, 邵良杉, 等. 基于微博短文本的用户兴趣建模方法[J]. 计算机工程, 2014, 40(2): 275-279.
doi: 10.3969/j.issn.1000-3428.2014.02.060
[9] (Qiu Yunfei, Wang Linying, Shao Liangshan, et al.User Interest Modeling Approach Based on Short Text of Micro-blog[J]. Computer Engineering, 2014, 40(2): 275-279.)
doi: 10.3969/j.issn.1000-3428.2014.02.060
[10] 宋巍, 张宇, 谢毓彬, 等. 基于微博分类的用户兴趣识别[J]. 智能计算机与应用, 2013, 3(4): 80-83.
doi: 10.3969/j.issn.2095-2163.2013.04.021
[10] (Song Wei, Zhang Yu, Xie Yubin, et al.Identifying User Interests Based on Microblog Classification[J]. Intelligent Computer and Applications, 2013, 3(4): 80-83.)
doi: 10.3969/j.issn.2095-2163.2013.04.021
[11] 杨福强, 王洪国, 董树霞, 等. 基于微博扩展的用户兴趣主题挖掘算法[J]. 计算机工程与设计, 2015, 36(5): 1214-1218.
[11] (Yang Fuqiang, Wang Hongguo, Dong Shuxia, et al.Topic Mining Algorithm of User Interest Based on Weibo Extension[J]. Computer Engineering Design, 2015, 36(5): 1214-1218.)
[12] 黎荆妗. 微博文本预处理与用户兴趣建模方法研究[D]. 重庆: 重庆大学, 2015.
[12] (Li Jingjin.Research on the Approach of Micro-blog Text Preprocessing and User Interest Modeling[D]. Chongqing: Chongqing University, 2015.)
[13] 易明, 毛进, 邓卫华. 基于社会化标签网络的细粒度用户兴趣建模[J]. 现代图书情报技术, 2011(4): 35-41.
[13] (Yi Ming, Mao Jin, Deng Weihua.Fine-grained User Preference Modeling Based on Tag Networks[J]. New Technology of Library and Information Service, 2011(4): 35-41.)
[14] 扈维, 张尧学, 周悦芝. 基于社会化标注的用户兴趣挖掘[J]. 清华大学学报:自然科学版, 2014, 54(4): 502-507.
[14] (Hu Wei, Zhang Yaoxue, Zhou Yuezhi.User Preference Mining Based on Social Tagging[J]. Journal of Tsinghua University: Science and Technology, 2014, 54(4): 502-507.)
[15] 孙雨生, 刘伟, 仇蓉蓉, 等. 国内用户兴趣建模研究进展[J]. 情报杂志, 2013, 32(5): 145-149.
doi: 10.3969/j.issn.1002-1965.2013.05.027
[15] (Sun Yusheng, Liu Wei, Qiu Rongrong, et al.Research Development of User Interest Modeling in China[J]. Journal of Intelligence, 2013, 32(5): 145-149.)
doi: 10.3969/j.issn.1002-1965.2013.05.027
[16] 万华林, Chowdhury M U.基于支持向量机的图像语义分类[J]. 软件学报, 2003, 14(11): 1891-1899.
[16] (Wan Hualin, Chowdhury M U.Image Semantic Classification by Using SVM[J]. Journal of Software, 2003, 14(11): 1891-1899.)
[17] 高隽, 谢昭, 张骏, 等. 图像语义分析与理解综述[J]. 模式识别与人工智能, 2010, 23(2): 191-202.
doi: 10.3969/j.issn.1003-6059.2010.02.010
[17] (Gao Jun, Xie Zhao, Zhang Jun, et al.A Review on Image Semantic Analysis and Understanding[J]. Pattern Recognition and Artificial Intelligence, 2010, 23(2): 191-202.)
doi: 10.3969/j.issn.1003-6059.2010.02.010
[18] Lin C Y, Yin J X, Gao X, et al.A Semantic Modeling Approach for Medical Image Semantic Retrieval Using Hybrid Bayesian Networks[C]//Proceedings of the 6th International Conference on Intelligent Systems Design and Applications. IEEE, 2006.
[19] Wang B, Zhang X, Zhao Z Y, et al.A Semantic Description for Content-based Image Retrieval[C]// Proceedings of the 2008 International Conference on Machine Learning and Cybernetics. IEEE, 2008.
[20] Yao T, Long F, Mei T, et al.Deep Semantic-preserving and Ranking-based Hashing for Image Retrieval[C]//Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2016: 3931-3937.
[21] Qazi N, Wong B L W. Semantic Based Image Retrieval Through Combined Classifiers of Deep Neural Network and Wavelet Decomposition of Image Signal[C]// Proceedings of the 8th Eurosim Congress on Modelling and Simulation.2016.
[22] Szegedy C, Liu W, Jia Y, et al.Going Deeper with Convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2015: 1-9.
[23] You Q, Bhatia S, Sun T, et al.The Eyes of the Beholder: Gender Prediction Using Images Posted in Online Social Networks[C]//Proceedings of the 2014 IEEE International Conference on Data Mining Workshop. 2014:1026-1030.
[24] You Q, Bhatia S, Luo J.A Picture Tells a Thousand Words-About You! User Interest Profiling from User Generated Visual Content[J]. Signal Processing, 2015, 124(C): 45-53.
doi: 10.1016/j.sigpro.2015.10.032
[25] Segalin C, Dong S C, Cristani M.Social Profiling Through Image Understanding: Personality Inference Using Convolutional Neural Networks[J]. Computer Vision and Image Understanding, 2016, 156: 34-50.
doi: 10.1016/j.cviu.2016.10.013
[26] Yang Y, Wang X, Guan T, et al.A Multi-dimensional Image Quality Prediction Model for User-generated Images in Social Networks[J]. Information Sciences, 2014, 281: 601-610.
doi: 10.1016/j.ins.2014.03.016
[27] Yang Y, Jia J, Wu B, et al.Social Role-Aware Emotion Contagion in Image Social Networks[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2016: 65-71.
[28] Sasaki W, Furukawa Y, Nishiyama Y, et al.SmileWave: Sensing and Analysis of Smile-based Emotional Contagion over Social Network: Poster Abstract[C]//Proceedings of the 15th ACM International Conference on Information Processing in Sensor Networks. 2016.
[29] Chang C C, Lin C J. LIBSVM: A Library for Support Vector Machines[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2011, 2(3): Article No. 27.
doi: 10.1145/1961189.1961199
[30] Rätsch G, Onoda T, Müller K R.Soft Margins for AdaBoost[J]. Machine Learning, 2001, 42(3): 287-320.
doi: 10.1023/A:1007618119488
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