Estimating interference in the QPRP for subtopic retrieval

Guido Zuccon, Leif Azzopardi, C. Hauff, C.J. Keith van Rijsbergen

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    5 Citations (Scopus)

    Abstract

    The Quantum Probability Ranking Principle (QPRP) has been recently proposed, and accounts for interdependent document relevance when ranking. However, to be instantiated, the QPRP requires a method to approximate the "interference" between two documents. In this poster, we empirically evaluate a number of different methods of approximation on two TREC test collections for subtopic retrieval. It is shown that these approximations can lead to significantly better retrieval performance over the state of the art.
    Original languageUndefined
    Title of host publicationProceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval
    Place of PublicationNew York
    PublisherAssociation for Computing Machinery (ACM)
    Pages741-742
    Number of pages2
    ISBN (Print)978-1-4503-0153-4
    DOIs
    Publication statusPublished - Jul 2010
    Event33rd Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010 - Geneva, Switzerland
    Duration: 19 Jul 201023 Jul 2010
    Conference number: 33

    Publication series

    Name
    PublisherACM

    Conference

    Conference33rd Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010
    Abbreviated titleSIGIR
    CountrySwitzerland
    CityGeneva
    Period19/07/1023/07/10

    Keywords

    • METIS-270946
    • IR-72485
    • Quantum Probability Ranking Principle
    • CR-H.3
    • Diversity
    • EWI-18227
    • Interference estimation

    Cite this

    Zuccon, G., Azzopardi, L., Hauff, C., & van Rijsbergen, C. J. K. (2010). Estimating interference in the QPRP for subtopic retrieval. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval (pp. 741-742). [10.1145/1835449.1835593] New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/1835449.1835593