Tuning before feedback: combining ranking function discovery and blind feedback for robust retrieval

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Fan2004sigir,
  author =       "Weiguo Fan and Ming Luo and Li Wang and Wensi Xi and 
                 Edward A. Fox",
  title =        "Tuning before feedback: combining ranking function
                 discovery and blind feedback for robust retrieval",
  booktitle =    "the Proceedings of the 27th Annual International ACM
                 SIGIR Conference",
  year =         "2004",
  pages =        "138--145",
  address =      "Sheffield, United Kingdom",
  publisher_address = "New York, NY, USA",
  month =        "25-29 " # jul,
  organisation = "SIGIR",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, intelligent
                 information retrieval, search engine, ranking function
                 discovery, information retrieval, blind feedback",
  ISBN =         "1-58113-881-4",
  URL =          "http://filebox.vt.edu/users/wfan/paper/ARRANGER/p52-Fan.pdf",
  URL =          "http://doi.acm.org/10.1145/1008992.1009018",
  DOI =          "doi:10.1145/1008992.1009018",
  size =         "8 pages",
  abstract =     "Both ranking functions and user queries are very
                 important factors affecting a search engine's
                 performance. Prior research has looked at how to
                 improve ad-hoc retrieval performance for existing
                 queries while tuning the ranking function, or modify
                 and expand user queries using a fixed ranking scheme
                 using blind feedback. However, almost no research has
                 looked at how to combine ranking function tuning and
                 blind feedback together to improve ad-hoc retrieval
                 performance. In this paper, we look at the performance
                 improvement for ad-hoc retrieval from a more integrated
                 point of view by combining the merits of both
                 techniques. In particular, we argue that the ranking
                 function should be tuned first, using user-provided
                 queries, before applying the blind feedback technique.
                 The intuition is that highly-tuned ranking offers more
                 high quality documents at the top of the hit list, thus
                 offers a stronger baseline for blind feedback. We
                 verify this integrated model in a large scale
                 heterogeneous collection and the experimental results
                 show that combining ranking function tuning and blind
                 feedback can improve search performance by almost 30
                 percent over the baseline Okapi system.",
  notes =        "http://www.sigir.org/sigir2004/

                 Also known as \cite{Fan:2004:TBF:1008992.1009018}",
}

Genetic Programming entries for Weiguo Fan Ming Luo Li Wang Wensi Xi Edward A Fox

Citations