Probabilistic latent query analysis for combining multiple retrieval sources

  • Authors:

  • Rong Yan;Alexander G. Hauptmann

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Combining the output from multiple retrieval sources over the same document collection is of great importance to a number of retrieval tasks such as multimedia retrieval, web retrieval and meta-search. To merge retrieval sources adaptively according to query topics, we propose a series of new approaches called probabilistic latent query analysis (pLQA), which can associate non-identical combination weights with latent classes underlying the query space. Compared with previous query independent and query-class based combination methods, the proposed approaches have the advantage of being able to discover latent query classes automatically without using prior human knowledge, to assign one query to a mixture of query classes, and to determine the number of query classes under a model selection principle. Experimental results on two retrieval tasks, i.e., multimedia retrieval and meta-search, demonstrate that the proposed methods can uncover sensible latent classes from training data, and can achieve considerable performance gains.