Mining actionable knowledge on the Web

Qiang Yang; Craig A. Knoblock; Xindong Wu

doi:10.1109/MIS.2004.64

Mining actionable knowledge on the Web

Qiang Yang^*, Craig A. Knoblock, Xindong Wu

^*Corresponding author for this work

Department of Computer Science and Engineering

Research output: Contribution to journal › Review article › peer-review

1 Citation (Scopus)

Abstract

Various techniques that can address the problem of actionable web mining, which is a research frontier for both data mining and Web information exploration, are discussed. VISCORS, a visual-content recommender for the mobile web, presents an algorithm for applying collaborative filtering to deliver Web content to mobile users. Collaborative Filtering with Maximum Entropy provides a novel maximum-entropy approach for generating online recommendations as a user navigates through a collection of documents. Mining Web Pages for Data Records presents a method for extracting structured data records from unstructured data sources such as HTML files.

Original language	English
Pages (from-to)	30-31
Number of pages	2
Journal	IEEE Intelligent Systems
Volume	19
Issue number	6
DOIs	https://doi.org/10.1109/MIS.2004.64
Publication status	Published - 1 Nov 2004

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10.1109/MIS.2004.64

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title = "Mining actionable knowledge on the Web",

abstract = "Various techniques that can address the problem of actionable web mining, which is a research frontier for both data mining and Web information exploration, are discussed. VISCORS, a visual-content recommender for the mobile web, presents an algorithm for applying collaborative filtering to deliver Web content to mobile users. Collaborative Filtering with Maximum Entropy provides a novel maximum-entropy approach for generating online recommendations as a user navigates through a collection of documents. Mining Web Pages for Data Records presents a method for extracting structured data records from unstructured data sources such as HTML files.",

author = "Qiang Yang and Knoblock, \{Craig A.\} and Xindong Wu",

year = "2004",

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journal = "IEEE Intelligent Systems",

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AU - Yang, Qiang

AU - Knoblock, Craig A.

AU - Wu, Xindong

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N2 - Various techniques that can address the problem of actionable web mining, which is a research frontier for both data mining and Web information exploration, are discussed. VISCORS, a visual-content recommender for the mobile web, presents an algorithm for applying collaborative filtering to deliver Web content to mobile users. Collaborative Filtering with Maximum Entropy provides a novel maximum-entropy approach for generating online recommendations as a user navigates through a collection of documents. Mining Web Pages for Data Records presents a method for extracting structured data records from unstructured data sources such as HTML files.

AB - Various techniques that can address the problem of actionable web mining, which is a research frontier for both data mining and Web information exploration, are discussed. VISCORS, a visual-content recommender for the mobile web, presents an algorithm for applying collaborative filtering to deliver Web content to mobile users. Collaborative Filtering with Maximum Entropy provides a novel maximum-entropy approach for generating online recommendations as a user navigates through a collection of documents. Mining Web Pages for Data Records presents a method for extracting structured data records from unstructured data sources such as HTML files.

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UR - https://www.scopus.com/pages/publications/10944248869

U2 - 10.1109/MIS.2004.64

DO - 10.1109/MIS.2004.64

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SN - 1541-1672

VL - 19

SP - 30

EP - 31

JO - IEEE Intelligent Systems

JF - IEEE Intelligent Systems

IS - 6

ER -

Mining actionable knowledge on the Web

Abstract

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