Relevance feedback with a small number of relevance judgements: incremental relevance feedback vs. document clustering

ABSTRACT

The use of incremental relevance feedback and document clustering were investigated in an relevance feedback environment in which the number of relevance judgements was quite small. Through experiments on the TREC collection, the incremental relevance feedback approach was found not to improve the overall search effectiveness. The clustering approach was found to be promising, although it sometimes over-focuses on a particular topic in a query and ignores the others. To overcome this problem, a query-biased clustering algorithm was developed and shown to be effective.

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INDEX TERMS

Classification:
  D. Software
  D.3 PROGRAMMING LANGUAGES
      D.3.2 Language Classifications

          Nouns: UML

  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.1 Multimedia Information Systems
          Subjects: Animations

  I. Computing Methodologies
  I.3 COMPUTER GRAPHICS
      I.3.6 Methodology and Techniques
          Subjects: Interaction techniques
      I.3.7 Three-Dimensional Graphics and Realism
          Subjects: Virtual reality

General Terms:
Algorithms, Design, Experimentation, Human Factors, Languages, Performance, Reliability, Theory