Adaptive clustering of search results

Xuehua Shen, Chengxiang Zhai, Nicholas J. Belkin

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Clustering of search results has been shown to be advantageous over the simple list presentation of search results. However, in most clustering interfaces, the clusters are not adaptive to a user's interaction with the clustering results, and the important question "how to optimize the benefit of a clustering interface for a user" has not been well addressed in the previous work. In this paper, we study how to exploit a user's clickthrough information to adaptively reorganize the clustering results and help a user find the relevant information more quickly. We propose four strategies for adapting clustering results based on user actions. We propose a general method to simulate different kinds of users and linearize the cluster results so that we can compute regular retrieval measures. The simulation experiments show that the adaptation strategies have different performance for different types of users; in particular, they are effective for "smart users" who can correctly recognize the best clusters, but not effective for "dummy users" who follow system's ranking of results. We further conduct a user study on one of the four adaptive clustering strategies to see if an adaptive clustering system using such a strategy can bring users better search experience than a static clustering system. The results show that there is generally no significant difference between the two systems from a user's perspective.

Original languageEnglish (US)
Title of host publicationUser Modeling, Adaptation, and Personalization - 17th International Conference, UMAP 2009 formerly UM and AH, Proceedings
Number of pages7
StatePublished - 2009
Event17th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2009 - Trento, Italy
Duration: Jun 22 2009Jun 26 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5535 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other17th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2009

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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