By Topic

Internet Users' Psychosocial Attention Prediction: Web Hot Topic Prediction Based on Adaptive AR Model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Hengqing Tong ; Dept. of Math., Wuhan Univ. of Technol., Wuhan ; Yang Liu ; Hui Peng ; Jing Tang

Web hot topic prediction is now one of the most significant research focus in Web data mining, which can reflect the Internet users' psychosocial predilection, may greatly benefit us. Markov and neural network are such two typical traditional prediction model, however, the Markov method can neither capture nor express the statistical property of the real data while the computation of neural network is quite complex. In this paper, a new method based on adaptive auto regession (AR) model is proposed, the parameter estimation algorithm of this model is referred to as recursive weighted least square (RWLS) and therefore defines the topic trend according to the model, and the computation is simple and quick. Also included are the advantages and shortcomings of this method.

Published in:

Computer Science and Information Technology, 2008. ICCSIT '08. International Conference on

Date of Conference:

Aug. 29 2008-Sept. 2 2008