Using the quantum probability ranking principle to rank interdependent documents

Zuccon, G. and Azzopardi, L. (2010) Using the quantum probability ranking principle to rank interdependent documents. Lecture Notes in Computer Science, 5993, pp. 357-369. (doi: 10.1007/978-3-642-12275-0_32)

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Abstract

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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Azzopardi, Dr Leif
Authors: Zuccon, G., and Azzopardi, L.
Subjects:Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
Publisher:Springer
ISSN:0302-9743
ISSN (Online):1611-3349

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