An empirical study of learning-based web search

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Although there are various approaches to facilitate the information search on the Web, most current Web search and query systems only return URLs of relevant pages. Learning-based Web search is invented targeting at processing the URLs to dig out the desired information by utilizing user feedback. However, the involvement of user behavior makes the study of system performance rather complex. In this paper, we introduce the empirical study of a learning-based Web query processing system, named FACT. Four major aspects of user behavior, namely, selection rule, training strategy, training size and training iteration, are considered to show their effects on the learning results. The experimental results are presented, together with analysis for the relationships between user behavior and system performance, which are important for further improvement on learning-based Web search technology.

Original languageEnglish
Title of host publicationAdvances in Web-Age Information Management - 3rd International Conference, WAIM 2002, Proceedings
EditorsXiaofeng Meng, Jianwen Su, Yujun Wang
PublisherSpringer Verlag
Pages116-125
Number of pages10
ISBN (Print)9783540440451
DOIs
StatePublished - 2002
Externally publishedYes
Event3rd International Conference on Advances in Web-Age Information Management, WAIM 2002 - Beijing, China
Duration: 11 Aug 200213 Aug 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2419
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Conference on Advances in Web-Age Information Management, WAIM 2002
Country/TerritoryChina
CityBeijing
Period11/08/0213/08/02

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