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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (2): 80-86    DOI: 10.11925/infotech.2096-3467.2017.02.11
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Constructing Users Profiles with Content and Gesture Behaviors
Qiangbing Wang1,2,Chengzhi Zhang1,2,3()
1School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094, China
2Jiangsu Collaborative Innovation Center of Social Safety Science and Technology, Nanjing 210094, China
3Jiangsu Key Laboratory of Data Engineering and Knowledge Service (Nanjing University), Nanjing 210093, China
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[Objective] This paper constructs users profiles by gauging their interests from gesture behaviors and related contents from a mobile article reading system. [Context] Users profiles construction with content and gesture behaviors can identifies users’ mobile reading interests and profiles effectively. [Methods] First, we collected user gesture behaviors (such as tap, double tap, swipe, drag, pinch in/out) as well as corresponding contents from a mobile article reading system. Second, we established the users model based on the collected data and reading time. [Results] Users could find their own reading interests while browsing papers with our system, which help us build users profiles. [Conclusions] Users gesture behaviors reveal their reading interests, which could improve the performance of marketing and personalized recommendation systems.

Key wordsGesture Behaviors      Mobile Device      Text Mining      User Modeling     
Received: 25 August 2016      Published: 27 March 2017

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Qiangbing Wang,Chengzhi Zhang. Constructing Users Profiles with Content and Gesture Behaviors. Data Analysis and Knowledge Discovery, 2017, 1(2): 80-86.

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[1] Guo Q, Jin H, Lagun D, et al.Mining Touch Interaction Data on Mobile Devices to Predict Web Search Result Relevance[C]//Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2013: 153-162.
[2] Han S G, Hsiao I H, Parra D.A Study of Mobile Information Exploration with Multi-Touch Interactions[C]//Proceedings of the 7th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction. 2014: 269-276.
[3] Joachims T, Granka L, Pan B, et al.Accurately Interpreting Clickthough Data as Implicit Feedback[C]//Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2005: 154-161.
[4] 孙铁利, 杨凤芹. 根据用户隐式反馈建立和更新用户兴趣模型[J]. 东北师大学报: 自然科学版, 2003, 35(3): 99-104.
[4] (Sun Tieli, Yang Fengqin.An Approach of Building and Updating User Interest Profile According to the Implicit Feedback[J]. Journal of Northeast Normal University: Natural Science Edition, 2003, 35(3): 99-104.)
[5] 赵银春, 付关友, 朱征宇. 基于Web 浏览内容和行为相结合的用户兴趣挖掘[J]. 计算机工程, 2005, 31(12): 93-94.
[5] (Zhao Yinchun, Fu Guanyou, Zhu Zhengyu.User Interest Mining of Combining Web Content and Behavior Analysis[J]. Computer Engineering, 2005, 31(12): 93-94.)
[6] Huang J, White R W, Dumais S T. No Clicks, No Problem: Using Cursor Movements to Understand and Improve Search[C]// Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2011: 1225-1234.
[7] Han S G, Yue Z, He D Q. Understanding and Supporting Cross-Device Web Search for Exploratory Tasks with Mobile Touch Interactions [J]. ACM Transactions on Information Systems, 2015, 33(4): Article No. 16.
[8] Morita M, Shinoda Y.Information Filtering Based on User Behavior Analysis and Best Match Text Retrieval[C]// Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 1994: 272-281.
[9] Han S. PITT (PIck-up The Touches) -- A Javascript Plugin to Track Your Website Visits [EB/OL]. [2016-06-25]. .
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