Using the quantum probability ranking principle to rank interdependent documents

Guido Zuccon, Leif Azzopardi

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

72 Citations (Scopus)


A known limitation of the Probability Ranking Principle (PRP) is that it does not cater for dependence between documents. Recently, the Quantum Probability Ranking Principle (QPRP) has been proposed, which implicitly captures dependencies between documents through “quantum interference”. This paper explores whether this new ranking principle leads to improved performance for subtopic retrieval, where novelty and diversity is required. In a thorough empirical investigation, models based on the PRP, as well as other recently proposed ranking strategies for subtopic retrieval (i.e. Maximal Marginal Relevance (MMR) and Portfolio Theory(PT)), are compared against the QPRP. On the given task, it is shown that the QPRP outperforms these other ranking strategies. And unlike MMR and PT, one of the main advantages of the QPRP is that no parameter estimation/tuning is required; making the QPRP both simple and effective. This research demonstrates that the application of quantum theory to problems within information retrieval can lead to significant improvements.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval
Subtitle of host publication32nd European Conference on IR Research, ECIR 2010, Milton Keynes, UK, March 28-31, 2010.Proceedings
Place of PublicationBerlin, Heidelberg
Number of pages13
ISBN (Print)3-642-12274-4, 978-3-642-12274-3
Publication statusPublished - 2010
Externally publishedYes

Publication series

NameLecture Notes in Computer Science


  • quantum probability ranking principle
  • information retrieval


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