IRC: An Iterative Reinforcement Categorization Algorithm for Interrelated Web Objects

Authors:
Gui-Rong Xue;Dou Shen;Qiang Yang;Hua-Jun Zeng;Zheng Chen;Yong Yu;WenSi Xi;Wei-Ying Ma
Affiliations:
Shanghai Jiao-Tong University, P.R. China;TsingHua University, Beijing, P.R. China;Hong Kong University of Science and Technology;Microsoft Research Asia, Beijing, P.R. China;Microsoft Research Asia, Beijing, P.R. China;Shanghai Jiao-Tong University, P.R. China;Virginia Polytechnic Institute and State University;Microsoft Research Asia, Beijing, P.R. China
Venue:
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Year:
2004

Citing 0
Cited 8

A study on combination of block importance and relevance to estimate page relevance

WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
A comparison of implicit and explicit links for web page classification

Proceedings of the 15th international conference on World Wide Web
Learning query intent from regularized click graphs

Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Iterative Reinforcement Cross-Domain Text Classification

ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Exploring social tagging graph for web object classification

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning with click graph for query intent classification

ACM Transactions on Information Systems (TOIS)
Learning search tasks in queries and web pages via graph regularization

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A novel web page categorization algorithm based on block propagation using query-log information

WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management

Quantified Score

Hi-index	0.01

Visualization

Abstract

Most existing categorization algorithms deal with homogeneous Web data objects, and consider interrelated objects as additional features when taking the interrelationships withother types of objects into account. However, focusing on any single aspects of these interrelationships and objects will not fully reveal their true categories. In this paper, wepropose a novel categorization algorithm, the Iterative Reinforcement Categorization algorithm (IRC), to exploit the full interrelationships between the heterogeneous objects on the Web.IRC attempts to classify the interrelated Web objects by iterative reinforcement between individual classification results of different types via the interrelationships. Experiments on a clickthrough log dataset from MSN search engine show that, with the F1 measures, IRC achieves a 26.4% improvement over a pure content-based classification method, a 21% improvement over a query metadata-based method, and a 16.4% improvement over a virtual document-based method. Furthermore, our experiments show that IRC converges rapidly.