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

Authors:
Shen Huang;Yong Yu;Shengping Li;Gui-Rong Xue;Lei Zhang
Affiliations:
Shanghai Jiao Tong University, Shanghai, P.R. China;Shanghai Jiao Tong University, Shanghai, P.R. China;Shanghai Jiao Tong University, Shanghai, P.R. China;Shanghai Jiao Tong University, Shanghai, P.R. China;Shanghai Jiao Tong University, Shanghai, P.R. China
Venue:
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Year:
2005

Citing 8
Cited 2

Term-weighting approaches in automatic text retrieval

Information Processing and Management: an International Journal
A language modeling approach to information retrieval

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Record-boundary discovery in Web documents

SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Discovering informative content blocks from Web documents

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning block importance models for web pages

Proceedings of the 13th international conference on World Wide Web
TSSP: A Reinforcement Algorithm to Find Related Papers

WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
IRC: An Iterative Reinforcement Categorization Algorithm for Interrelated Web Objects

ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Extracting content structure for web pages based on visual representation

APWeb'03 Proceedings of the 5th Asia-Pacific web conference on Web technologies and applications

Enhancing web page classification through image-block importance analysis

Information Processing and Management: an International Journal
Exploiting link analysis with a three-layer web structure model

WISE'06 Proceedings of the 7th international conference on Web Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Some work showed that segmenting web pages into "semantic independent" blocks could help to improve the whole page retrieval. One key and unexplored issue is how to combine the block importance and relevance to a given query. In this poster, we first propose an automatic way to measure block importance to improve retrieval. After that, user information need is also concerned to refine block importance for different users.