Library and Information Service

Library and Information Service >

2018 , Vol. 62 >Issue 13: 54 - 63

DOI: https://doi.org/10.13266/j.issn.0252-3116.2018.13.008

User Profiling Based on the Behaviour and Content Combined Model

  • Yu Chuanming ,
  • Tian Xin ,
  • Guo Yajing ,
  • An Lu
Expand
  • 1. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073;
    2. School of Information Management, Wuhan University, Wuhan 430072

Received date: 2018-01-04

  Revised date: 2018-04-02

  Online published: 2018-07-05

Fold

Abstract

[Purpose/significance] To identify and remove online reviews from irrational investors, enhance the professional degree and quality of comments, and to promote rational investment, this article takes identifying whether the users on the Guba website belong to the noise investors as an example, and carries out a user profiling study.[Method/process] Deep user representation learning method was used to learn text information such as users'posts, then a behavior and content combined model was proposed with respect to behavior characteristics such as fans number, influence, bar age, post number and so on, and an empirical and comparative study was done on the annotated data set.[Result/conclusion] Experiment result showed that the BCCM model got the F1 score of 79.47%, which is superior to Decision Tree model(69.90%), SVM model(75.61%), KNN model(73.21%) and ANN model(74.83%). In the specific user profiling task of identifying noise traders, by using deep user representation learning method to obtain text content characteristics, the various evaluation metrics of use profiling can be remarkably improved.

Key words： user modelling; emotional analysis; user representation learning; characteristic fusion

Cite this article

Yu Chuanming , Tian Xin , Guo Yajing , An Lu . User Profiling Based on the Behaviour and Content Combined Model[J]. Library and Information Service, 2018 , 62(13) : 54 -63 . DOI: 10.13266/j.issn.0252-3116.2018.13.008

References

[1] FERWERDA B, SCHEDL M. Personality-based user modeling for music recommender systems[C]//Joint European conference on machine learning and knowledge discovery in databases. Berlin, Heidelberg:Springer, Cham, 2016:254-257.
[2] YAN M, SANG J, XU C, et al. A unified video recommendation by cross-network user modeling[J]. ACM transactions on multimedia computing communications & applications, 2016,12(4):1-24.
[3] 王智囊. 基于用户画像的医疗信息精准推荐的研究[D]. 成都:电子科技大学, 2016.
[4] 吴明礼, 杨双亮. 基于移动特征数据的内容推送技术研究与应用[J]. 计算机技术与发展, 2017, 27(9):155-160.
[5] 赵曙光. 高转化率的社交媒体用户画像:基于500用户的深访研究[J]. 现代传播-中国传媒大学学报, 2014, 36(6):115-120.
[6] YU S, GUPTA A. Identifying decision makers from professional social networks[C]//ACM SIGKDD international conference on knowledge discovery and data mining. New York:ACM, 2016:333-342.
[7] TRUSOV M, MA L, JAMAL Z. Crumbs of the cookie:user profiling in customer-base analysis and behavioral targeting[J]. Marketing science, 2016, 35(3):405-426.
[8] HA I, OH K J, JO G S. Personalized advertisement system using social relationship based user modeling[J]. Multimedia tools & applications, 2015, 74(20):8801-8819.
[9] ELKAHKY A M, SONG Y, HE X. A multi-view deep learning approach for cross domain user modeling in recommendation systems[C]//International conference on world wide Web. Florence, Tuscany, Italy:International world wide Web conferences steering committee, 2015:278-288.
[10] CODINA V, MENA J, OLIVA L. Context-aware user modeling strategies for journey plan recommendation[M]//User modeling, adaptation and personalization. Berlin, Heidelberg:Springer international publishing, 2015:68-79.
[11] BANSAL T, DAS M, BHATTACHARYYA C. Content driven user profiling for comment-worthy recommendations of news and blog articles[C]//ACM conference on recommender systems. New York:ACM, 2015:195-202.
[12] PIAO G, BRESLIN J G. User modeling on Twitter with word net synsets and DBpedia concepts for personalized recommendations[C]//ACM international on conference on information and knowledge management. New York:ACM, 2016:2057-2060.
[13] PIAO G, BRESLIN J G. Exploring dynamics and semantics of user interests for user modeling on Twitter for link recommendations[C]//International conference on semantic systems. New York:ACM, 2016:81-88.
[14] 汪强兵, 章成志. 融合内容与用户手势行为的用户画像构建系统设计与实现[J]. 数据分析与知识发现, 2017, 1(2):80-86.
[15] 黄文彬, 徐山川, 吴家辉,等. 移动用户画像构建研究[J]. 现代情报, 2016, 36(10):54-61.
[16] DONG Y X, CHAWLA N V, TANG J,et al. User modeling on demographic attributes in big mobile social networks[J]. Acm transactions on information systems, 2017, 35(4):1-34.
[17] TANG D, QIN B, YANG Y, et al. User modeling with neural network for review rating prediction[C]//International conference on artificial intelligence. Palo Alto, CA, USA:AAAI Press, 2015:1340-1346.
[18] PENG J, CHOO K K R, ASHMAN H. User profiling in intrusion detection:a review[J]. Journal of network & computer applications, 2016, 72(1):14-27.
[19] FARSEEV A, NIE L, AKBARIk M, et al. Harvesting multiple sources for user profile learning:a big data study[C]//ACM on international conference on multimedia retrieval. New York:ACM, 2015:235-242.
[20] KYLE A S. Market structure, information, futures markets, and price formation[M]//. International ag-ricultural trade advanced reading in price formation market structure & price instability. Boulder, Colorado, USA:Westview Press, 1984:45-64.
[21] LONG J B D, SHLEIFER A, SUMMERS L H, et al. Noise trader risk in financial markets[J]. Journal of political economy, 1990, 98(4):703-738.
[22] LEE C M C, SHLEIFER A, THALER R H. Investor sentiment and the closed-end fund puzzle[J]. Journal of finance, 1991,46(1):75-109.
[23] 杨楷. 投资者情绪与股市短期波动的关系研究——对2015年中国股市的考察[J]. 未来与发展, 2016, 40(10):62-67.
[24] SILVA E M, TAKIMOTO L. How to model noise traders investors using prospect theory[J]. Open access library journal, 2017, 4(4):1-7.
[25] 孔东民. 中国股市投资者的策略研究:基于一个噪音交易模型[J]. 管理学报, 2008, 5(4):542-548.
[26] RECHENTHIN M, STREET W N, SRINIVASAN P. Stock chatter:using stock sentiment to predict price direction[J]. Algorithmic finance, 2014, 2(3):169-196.
[27] ACKERT L F, JIANG L, LEE H S, et al. Influential investors in online stock forums[J]. International review of financial analysis, 2016, 45(1):39-46.
[28] NGUYEN T H, SHIRAI K, VELCIN J. Sentiment analysis on social media for stock movement prediction[J]. Expert systems with applications, 2015, 42(24):9603-9611.
[29] FEUERRIEGEL S, NEUMANN D. Evaluation of news-based trading strategies[C]//International workshop on enterprise applications and services in the finance industry. Berlin, Heidelberg:Springer, Cham, 2014:13-28.
[30] 池丽旭, 张广胜, 庄新田,等. 投资者情绪指标与股票市场——基于扩展卡尔曼滤波方法的研究[J]. 管理工程学报, 2012, 26(3):122-128.
[31] 熊伟, 陈浪南. 股票特质波动率、股票收益与投资者情绪[J]. 管理科学, 2015(5):106-115.
[32] KHOLDY S, SOHRABIAN A. Noise traders and the rational investors:a comparison of the 1990s and the 2000s[J]. Journal of economic studies, 2015, 41(6):849-862.
[33] ZHANG X, ZHANG L. How does the internet affect the financial market? an equilibrium model of in-ternet-facilitated feedback trading[J]. MIS Quarterly, 2015, 39(1):17-38.
[34] 辛荣, 张强, 陈彬彬. 噪声交易者情绪、信息质量与市场多重进化均衡[J]. 系统工程, 2016(4):9-17.
[35] 王宜峰, 王燕鸣. 投资者情绪在资产定价中的作用研究[J]. 管理评论, 2014, 26(6):42-55.
[36] 彭叠峰, 饶育蕾, 雷湘媛. 有限关注、噪声交易与均衡资产价格[J]. 管理科学学报, 2015, 18(9):86-94.
[37] 刘毅, 李景华. 噪声交易者在金融市场的长期存在性研究[J]. 管理评论, 2012, 24(7):36-41.
[38] RAMIAH V, XU X, MOOSA I A. Neoclassical finance, behavioral finance and noise traders:a review and assessment of the literature[J]. International review of financial analysis, 2015, 41(1):89-100.
[39] SHIN J K, SUBRAMANIAN C. Monetary policy and noise traders:a welfare analysis[J]. Journal of macroeconomics, 2016, 49(C):33-45.
[40] 王凌霄, 沈卓, 李艳. 社会化问答社区用户画像构建[J]. 情报理论与实践, 2018, 41(1):129-134.
[41] 林燕霞,谢湘生.基于社会认同理论的微博群体用户画像[J]. 情报理论与实践, 2018, 41(3):142-148.
[42] TONG S, KOLLER D. Support vector machine active learning with applications to text classification[J]. Journal of machine learning research, 2001, 2(1):45-66.
[43] MCCALLUM A, NIGAM K. A comparison of event models for Naive Bayes text classification[C]//AAAI-98 workshop on learning for text categorization. Palo Alto, CA, USA:AAAI Press, 1998, 62(2):41-48.
[44] LANDGREBE D. A survey of decision tree classifier methodology[J]. IEEE transactions on systems, man, and cybernetics, 2002, 21(3):660-674.
[45] ZHANG M L, ZHOU Z H. ML-KNN:a lazy learning approach to multi-label learning[J]. Pattern recognition, 2007, 40(7):2038-2048.
[46] ZHANG G, PATUWO B E, HU M Y. Forecasting with artificial neural networks:the state of the art[J]. International journal of forecasting, 1998, 14(1):35-62.
[47] FRIEDMAN J, HASTIE T, TIBSHIRANI R. Additive logistic regression:a statistical view of boosting[J]. Annals of statistics, 2000, 28(2):337-374.
Options
Outlines

/

〈 〉

    Copyrights © LIS Press Co., Ltd. Reproduction is prohibited without permission.