A User Interest Model Based on the Analysis of User Behaviors
International Journal of Intelligent Information Systems
Volume 4, Issue 2-2, March 2015, Pages: 5-8
Received: Jan. 7, 2015; Accepted: Jan. 10, 2015; Published: Feb. 13, 2015
Views 3184      Downloads 154
Zhu Jinghua, College of Network Communication, Zhejiang Yuexiu University of Foreign Languages, Shaoxing, China
Article Tools
Follow on us
Understanding the users' interest is the base for the industralization of website. In order to provide individualized service better for the users, on the basis of analyzing the users' browse behavioral characteristics and according to the users' retention time in the page, and users' click frequency to the hyperlink and page, a model of computer user interest degree is established, and a neutral network is proposed to describe their correlation, and the reasonableness and effectiveness of this model are verified through experiment. The experiemtn result shows aathat this model can accurately find out the page that the users are interested in.
Individualization, User Browse Behavior, User Interest Degree, RBF Network
To cite this article
Zhu Jinghua, A User Interest Model Based on the Analysis of User Behaviors, International Journal of Intelligent Information Systems. Special Issue: Content-based Image Retrieval and Machine Learning. Vol. 4, No. 2-2, 2015, pp. 5-8. doi: 10.11648/j.ijiis.s.2015040202.12
Enrique Frias-Martinez, Sherry Y. Chen, Xiaohui Liu. Investigation of Behavior and Perception of Digital Library Users: A Cognitive Style Perspective[J]. International Journal of Information Management , 2008(28): 355-365.
Zhang Haitao, Jing Jipeng, Method of Determining Webpage Level According to User Browse Behavior [J]. Intelligence Journal, 2004, 23(3): 303-306.
Cheng Chih Chang, Pei-Ling Chen, Fei-Rung Chiu, et al. Application of Neural Networks and Kano’s Method to Content Recommendation in Web Personalization[J]. Expert Systems with Applications, 2008.
A. Georgakis, H. Li. User Behavior Modeling and Content Based Speculative Web Page Prefetching[J]. Data & Knowledge Engineering, 2006(59): 770-788.
Wang Jimin, Peng Bo, Analysis on Click Behavior of Search Engine Users [J]. Intelligence Journal, 2006(2): 154-162.
Feng-Hsu Wang, Hsiu-Mei Shao. Effective Personalized Recommendation Based on Time-Framed Navigation Clustering and Association Mining [J]. Expert Systems with Applications, 2004(27): 365-377.
Mrugank V Thakor, Wendy Borsuk, Maria Kalamas. Hotlists and Web Browsing Behavior - an Empirical Investigation [J]. Journal of Business Research, 2004(57): 776-786.
Zeng Chun, Xing Chunxiao, Zhou Lizhu, Technical Overview of Individualized Service [J]. Software Journal , 2002(10): 1952-1961.
Shuchih Emest Changa, S Wesley Changchiena. Assessing Users’ Product-Specific Knowledge for Personalization[J]. Expert Systems with Applications, 2006(30): 682-693.
Shu-Hsien Liao, Chih-Hao Wen, Artificial Neural Networks Classification and Clustering of Methodologies and Applications Literature Analysis From 1995 to 2005[J]. Expert Systems with Applications, 2007(32): 1-11.
Huang Xiaoyuan, Tian Peng, Securities Selection Decision-making Tools based on Neutral Network[J]. Application of Systematic Engineering Theory Method, 1995(2): 60-65.
Tan Qiong, Li Xiaoli, Shi Zongzhi, A Method to Realize the Individualzied Service of Search Engine[J]. Computer Science, 2002, 29(1): 23-25.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186