A user-centered approach to evaluating topic models

Diane Kelly, Fernando Diaz, Nicholas J. Belkin, James Allan

Research output: Chapter in Book/Report/Conference proceedingChapter

14 Scopus citations

Abstract

This paper evaluates the automatic creation of personal topic models using two language model-based clustering techniques. The results of these methods are compared with user-defined topic classes of web pages from personal web browsing histories from a 5-week period. The histories and topics were gathered during a naturalistic case study of the online information search and use behavior of two users. This paper further investigates the effectiveness of using display time and retention behaviors as implicit evidence for weighting documents during topic model creation. Results show that agglomerative techniques - specifically, average-link clustering - provide the most effective methodology for building topic models while ignoring topic evidence and implicit evidence.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsSharon McDonald, John Tait
PublisherSpringer Verlag
Pages27-41
Number of pages15
ISBN (Print)3540213821, 9783540213826
DOIs
StatePublished - 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2997
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'A user-centered approach to evaluating topic models'. Together they form a unique fingerprint.

Cite this