[1]CUI Wanqiu,DU Junping,ZHOU Nan,et al.Social network cross-media searching and mining based on user intention[J].CAAI Transactions on Intelligent Systems,2017,12(6):761-769.[doi:10.11992/tis.201706075]
Copy

Social network cross-media searching and mining based on user intention

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 6 Page number: 761-769 Column: 综述 Public date: 2017-12-25
Title:
Social network cross-media searching and mining based on user intention
Author(s):
CUI Wanqiu;  DU Junping;  ZHOU Nan;  LIANG Meiyu
Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China
Keywords:
online social network; user searching intention; knowledge graph; deep semantic learning; precise searching
CLC:
TP393
DOI:
10.11992/tis.201706075
Abstract:
With the popularity of online social networks, users not only have higher requirements for speed and real-time performance of information acquisition but also increased demand for personalized and accurate searching. To improve the quality of the search engine and accuracy of the result list, it is necessary to deeply mine the search intentions of the users. This paper summarizes the current situation in precise cross-media searching and mining based on user search intentions. We focus on multi-modal information perceptions based on an online social network knowledge graph, deep semantic learning and analysis of cross-media data for user search intention matching, and precise online social network searching and mining based on users’ search intentions. Finally, future research problems and possible challenges are discussed.
References:
[1] XIE H, LI X, WANG T, et al. Personalized search for social media via dominating verbal context[J]. Neurocomputing, 2016, 172(C): 27-37.
[2] LI J, LIU C, YU J X, et al. Personalized influential topic search via social network summarization[J]. IEEE transactions on knowledge and data engineering, 2016, 28(7): 1820-1834.
[3] KOU F, DU J, HE Y, et al. A survey on social network search based on semantic analysis and learning[J]. CAAI transactions on intelligence technology, 2016(1): 293-302.
[4] 石磊, 杜军平, 周亦鹏, 等. 在线社交网络挖掘与搜索技术研究[J]. 智能系统学报, 2016, 11(6): 777-787.
SHI Lei, DU Junping, ZHOU Yipeng, et al. A survey on online social network mining and search[J]. CAAI transactions on intelligence technology, 2016, 11(6): 777-787.
[5] DEUFEMIA V, GRANATELLO M, MEROLA A, et al. Comparing classifiers for web user intent understanding[J]. Empowering organizations, 2016, 11(2): 147-159.
[6] CHRISTOPH K, MARTHA L, ALAN H. User intent in multimedia search: a survey of the state of the art and future challenges[J]. ACM computing surveys, 2016, 49(2): 1-37.
[7] KATO M. Web search query privacy, an end-user perspective[J]. Journal of information security, 2017, 8(1): 56-74.
[8] RISHIRAJ S R, RAHUL K, NILOY G, et al. Discovering and understanding word level user intent in web search queries[J]. World wide web, 2015, 30: 22-38.
[9] JIN Y K, MARK C, JAIME T, et al. Understanding how people interact with web search results that change in real-time using implicit feedback[C]//The 22nd ACM International Conference on Information and Knowledge Management. San Francisco, USA, 2014: 2321-2326.
[10] CHRISTOPH K, SUBHABRATA B, MARTHA L, et al. Uploader intent for online video: typology, inference and applications[J]. IEEE transactions on multimedia, 2015, 17(8): 1200-1212.
[11] ZHENG Y, CAPRA L, WOLFSON O, et al. Urban computing: concepts, methodologies and applications[J]. ACM transactions on intelligent systems and technology, 2014, 5(3): 38.
[12] ZHENG Y, LIU T, WANG Y, et al. Diagnosing new york city’s noises with ubiquitous data[C]//Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Seattle, Washington, 2014: 715-725.
[13] ZHENG Y. Trajectory data mining: an overview[J]. ACM transactions on intelligent systems and technology, 2015, 6(3): 29.
[14] SONG S, LI Q, ZHENG N. A spatio-temporal framework for related topic search in micro-blogging[C]//International Conference on Active Media Technology. Springer Berlin Heidelberg, 2010: 63-73.
[15] SONG S, LI Q, BAO H. Detecting dynamic association among twitter topics[C]//International Conference on World Wide Web. Lyon, France, 2012: 605-606.
[16] LIU A A, SU Y T, NIE W Z, et al. Hierarchical clustering multi-task learning for joint human action grouping and recognition[J]. IEEE trans pattern anal mach intell, 2017, 39(1): 102-114.
[17] CUI P, WANG F, YANG S, et al. Item-Level Social Influence Prediction with Probabilistic Hybrid Factor Matrix Factorization[C]//AAAI Conference on Artificial Intelligence 2011. San Francisco, USA, 2011: 34-45.
[18] CRANSHAW J, TOCH E, HONG J, et al. Bridging the gap between physical location and online social networks[C]//ACM International Conference on Ubiquitous Computing. Copenhagen, Denmark, 2010: 119-128.
[19] 余丽, 陆锋, 张恒才. 网络文本蕴涵地理信息抽取: 研究进展与展望[J]. 地球信息科学学报, 2015, 17(2): 127-134.
YU Li, LU Feng, ZHANG Hengcai. Extracting geographic information from web texts: status and development[J]. Journal of geo-information science, 2015, 17(2): 127-134.
[20] 王宇. 基于网络文本的地名空间模糊建模[D]. 南京: 南京师范大学, 2012.
WANG Yu. A dissertation submitted in partial fulfillment of the requirements for the degree of master of science[D]. Nanjing: Nanjing Normal University, 2012.
[21] JIN T, LI L, LI P, et al. Hidden conditional random fields for category and scene classification[J]. Journal of computational information systems, 2012, 8(2): 877-885.
[22] CHIEN B C, SHI H T, FU C H, et al. Automatic geotagging for personal photos with sharing images on social media networks[C]//Multidisciplinary Social Networks Research. Berlin: Springer Berlin Heidelberg, 2015: 495-508.
[23] SENGSTOCK C, GERTZ M. Latent geographic feature extraction from social media[C]//Proceedings of the 20th ACM International Conference on Advances in Geographic Information Systems. New York, USA, 2012: 149-158.
[24] HONG L, DAVISON B D. Empirical study of topic modeling in Twitter[C]//Proceedings of the ACM First Workshop on Social Media Analytics. Washington D C, USA, 2010: 80-88.
[25] SONG S, LI Q, ZHENG X. Detecting popular topics in micro-blogging based on a user interest-based model[J]. The complex system of management and control of state key laboratory, 2012(IJCNN): 1-8.
[26] ZHENG N, SONG S, BAO H. A temporal-topic model for friend recommendations in Chinese microblogging systems[J]. IEEE transactions on systems, man, and cybernetics: systems, 2015, 45(9): 1245-1253.
[27] BAO H, LI Q, LIAO S S, et al. A new temporal and social pmf-based method to predict users’ interests in micro-blogging[J]. Decision support systems, 2013, 55(3): 698-709.
[28] GAO H, LI Q, BAO H, et al. How shall we catch people’s concerns in micro-blogging[C]//Proceedings of the 21st ACM International Conference on World Wide Web. Lyon, France, 2012: 505-506.
[29] WANG Y, WANG X, DU J, et al. Object detection based on exemplar object expression[M]. Advanced Multimedia and Ubiquitous Engineering. Springer Berlin Heidelberg, 2015.
[30] WANG X, DU J, WU S. High-level semantic image annotation based on hot internet topics[J]. Multimedia tools and applications, 2015, 74(6): 2055-2084.
[31] WU L, GE Y, LIU Q, et al. Modeling the evolution of users’ preferences and social links in social networking services[J]. IEEE transactions on knowledge and data engineering, 2017 (99): 1-1.
[32] LIANG M, DU J, LIU H. Self-adaptive spatial image denoising model based on scale correlation and sure-let in the nonsubsampled contourlet transform domain[J]. Science China-information sciences, 2014, 57(9): 1-15.
[33] WANG X, DU J, WU S, et al. Cluster ensemble-based image segmentation[J]. International journal of advanced robotic systems, 2013, 10(4): 1-11.
[34] 欧阳逸, 郭斌, 何萌, 等. 微博事件感知与脉络呈现系统. 浙江大学学报: 工学版, 2016, 50(6): 1176-1182.
OUYANG Yi, GUO Bin, HE Meng, et al. Event sensing and vein presentation leveraging microblog[J]. Journal of Zhejiang university: engineering science, 2016, 50(6): 1176-1182.
[35] 刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3): 582-600.
LIU Jiao, LI Yang, DUAN Hong, et al. Knowledge graph construction technique[J]. Journal of computer research and development, 2016, 53(3): 582-600.
[36] BALAID A, ABD R M Z, HIKMI S N, et al. Knowledge maps[J]. International journal of information management, 2016, 36(3): 451-475.
[37] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[J]. Computer science, 2013, 2(12): 27-35.
[38] WU W, LI H, WANG H, et al. Probase: a probabilistic taxonomy for text understanding[C]//ACM SIGMOD International Conference on Management of Data. Scottsdale, USA, 2012: 481-492.
[39] HUA W, WANG Z, WANG H, et al. Short text understanding through lexical-semantic analysis[C]//International Conference on Data Engineering. Seoul, South Korea, 2015: 495-506.
[40] DAVID M, BLEI M I, JORDAN. Modeling annotated data[C]//Proceedings of 2003 ACM International Conference on Research on Development in Information Retrieval. Toronto: ACM Press, 2003: 1-8.
[41] BIAN J, YANG Y, CHUA T S. Multimedia summarization for trending topics in microblogs[C]//Proceedings of ACM International Conference on Information and Knowledge Management. San Francisco: ACM Press, 2013: 1807-1812.
[42] 王智愚. 社会化多媒体内容分析与摘要[D]. 北京: 清华大学, 2013: 1-119.
WANG Zhiyu. Social multimedia analysis and summarization[D]. Beijing: Tsinghua University, 2013: 1-119.
[43] WANG Y, WU F, SONG J, et al. Multi-modal mutual topic reinforce modeling or cross-media retrieval[C]//Proceedings of 2014 ACM International Conference on Multimedia. Orlando: ACM Press, 2014: 307-316.
[44] KANG C, XIANG S, LIAO S, et al. Learning consistent feature representation for cross-modal multimedia retrieval[J]. IEEE transactions on multimedia, 2015, 17(3): 370-381.
[45] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[C]// Proceedings of Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2012: 1097-1105.
[46] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. Arxiv preprint, arxiv, 2014: 1409-1556.
[47] ANDREJ K, ARMAND J, LI F. Deep fragment embeddings for bidirectional image sentence mapping[J]. Arxiv Preprint, Arxiv, 2014, 14(3): 1406-5679.
[48] AMIRHOSSEIN H, THOMAS M, CEES G, et al. Discovering semantic vocabularies for cross-media retrieval [C]//Proceedings of ACM SIGMM International Conference on Multimedia Retrieval. Shanghai: ACM Press, 2015: 131-138.
[49] JIANG XY, WU F, LI X, et al. Deep compositional cross-modal learning to rank via local-global alignment[C]//Proceedings of 2015 ACM International Conference on Multimedia. Brisbane, 2015: 69-78.
[50] MA L, LU ZD, SHANG LD, et al. Multimodal convolutional neural networks for matching image and sentence[C]//Proceedings of 2015 International Conference on Computer Vision. Boston, USA, 2015: 2623-2631.
[51] ZHAO F, HUANG Y, WANG L, et al. Deep semantic ranking based hashing for multi-label image retrieval[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA, 2015: 1556-1564.
[52] WANG J, SONG Y, LEUNG T, et al. Learning fine-grained image similarity with deep ranking[C]//IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014: 1386-1393.
[53] REN S, GIRSHICK R, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6): 1137-1149.
[54] QIAN SS, ZHANG TZ, HONG R, et al. Cross-domain collaborative learning in social multimedia[C]//Proceedings of 2015 ACM International Conference on Multimedia. Brisbane: ACM Press, 2015: 99-108.
[55] 柯圣财, 赵永威, 李弼程, 等. 基于卷积神经网络和监督核哈希的图像检索方法[J]. 电子学报, 2017, 45(1): 157-163.
KE Shengcai, ZHAO Yongwei, LI Bicheng, et al. Image retrieval based on convolutional neural network and kernel-based supervised hashing[J]. Acta electronica sinica, 2017, 45(1): 157-163.
[56] 贾焰, 甘亮, 李爱平, 等. 社交网络智慧搜索研究进展与发展趋势[J]. 通信学报, 2015, 36(12): 9-16.
JIA Yan, GAN Liang, LI Aiping, et al. Research progress and development trend of onfine social network smart search[J]. Journal on communications, 2015, 36(12): 9-16.
[57] PARK K, JEE H, LEE T, et al. Automatic extraction of user’s search intention from web search logs[J]. Multimedia tools and applications, 2012, 61(1): 1-18.
[58] GUPTA V, GARG N, GUPTA T. Search bot: search intention based filtering using decision tree based technique[C]//Third IEEE International Conference on Intelligent Systems, Modelling and Simulation. Kota Kinabalu, Malaysia, 2012: 49-54.
[59] 王大玲, 于戈, 鲍玉斌, 等. 基于用户搜索意图的Web网页动态泛化[J]. 软件学报, 2010, 21(5): 1083-1097.
WANG Daling, YU Ge, BAO Yubin, et al. Dynamically generalizing Web pages based on users’ search intentions [J]. Journal of software, 2010, 21(5): 1083-1097.
[60] FAN W, LI J, MA S, et al. Graph homomorphism revisited for graph matching[C]//Proceedings of the VLDB Endowment, 2010, 3(1/2): 1161-1172.
[61] 郑炜, 梁战平, 梁建. 面向用户意图的智能搜索引擎框架研究[J]. 现代图书情报技术, 2014, 30(3): 65-72.
ZHENG Wei, LIANG Zhanping, LIANG Jian. Research on user oriented intelligent search engine framework[J]. Library and information science, 2014, 30(3): 65-72.
[62] BENNETT P N, WHITE R W, CHU W, et al. Modeling the impact of short- and long-term behavior on search personalization[C]//Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieva. Portland, USA, 2012: 185-194.
[63] CHILTON L B, TEEVAN J. Addressing people’s information needs directly in a web search result page[C]//ACM International Conference on World Wide Web. Hyderabad, India, 2011: 27-36.
[64] CHIKERSAL P, PORIA S, CAMBRIA E, et al. Modelling public sentiment in twitter: using linguistic patterns to enhance supervised learning[C]//International Conference on Intelligent Text Processing and Computational Linguistics. Cairo, Egypt, 2015: 49-65.
[65] SMAILOVIC J, KRANJC J, GRCAR M, et al. Monitoring the Twitter sentiment during the Bulgarian elections[C]//IEEE International Conference on Data Science and Advanced Analytics. Paris, France, 2015:1-10.
[66] MANOS S, SYMEON P, GEORGIOS P, et al. Multimodal event detection and summarization in large scale image collections[C]//Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval. New York, USA, 2016: 421-422.
[67] GAO Y, ZHAO S, YANG Y, et al. Multimedia social event detection in microblog[M]//Multimedia Modeling. Springer International Publishing, 2015: 269-281.
[68] UNANKARD S, LI X, SHARAF M. Emerging event detection in social networks with location sensitivity[J]//World wide web-internet and web information systems, 2014, 18(5): 1-25.
[69] YANG Y, DU J, HE B. A novel ontology-based semantic retrieval model for food safety domain[J]. Chinese journal of electronics, 2013, 22(2): 247-252.
Similar References:

Memo

-

Last Update: 2018-01-03

Copyright © CAAI Transactions on Intelligent Systems