Supporting personalized ranking over categorical attributes

Gae won You, Seungwon Hwang, Hwanjo Yu

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This paper studies how to enable an effective ranked retrieval over data with categorical attributes, in particular, by supporting personalized ranked retrieval of highly relevant data. While ranked retrieval has been actively studied lately, existing efforts have focused only on supporting ranking over numerical or text data. However, many real-life data contain a large amount of categorical attributes, in combination with numerical and text attributes, which cannot be efficiently supported - unlike numerical attributes where a natural ordering is inherent, the existence of categorical attributes with no such ordering complicates both the formulation and processing of ranking. This paper studies the efficient and effective support of ranking over categorical data, as well as uniform support with other types of attributes.

Original languageEnglish
Pages (from-to)3510-3524
Number of pages15
JournalInformation sciences
Volume178
Issue number18
DOIs
Publication statusPublished - 2008 Sep 15

Fingerprint

Categorical
Ranking
Attribute
Processing
Retrieval
Nominal or categorical data
Formulation
Text

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Electrical and Electronic Engineering
  • Statistics, Probability and Uncertainty
  • Information Systems and Management
  • Information Systems
  • Computer Science Applications
  • Artificial Intelligence

Cite this

You, Gae won ; Hwang, Seungwon ; Yu, Hwanjo. / Supporting personalized ranking over categorical attributes. In: Information sciences. 2008 ; Vol. 178, No. 18. pp. 3510-3524.
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Supporting personalized ranking over categorical attributes. / You, Gae won; Hwang, Seungwon; Yu, Hwanjo.

In: Information sciences, Vol. 178, No. 18, 15.09.2008, p. 3510-3524.

Research output: Contribution to journalArticle

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