Using the Quantum Probability Ranking Principle to Rank Interdependent Documents

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.

Combining Word Semantics within Complex Hilbert Space for Information Retrieval

“…Common to all these proposals is the assumption that information objects (queries, documents, etc.) are represented in real-valued Hilbert spaces, even when the key modelling aspect is the quantum interference phenomenon (see for example Zuccon and Azzopardi [3]). Zuccon and Piwowarski have argued that this assumption is not imposed by the models themselves (which, being grounded on the mathematics of quantum theory, allow for complex valued representations), but is instead rooted in the difficulties of understanding how complex numbers could be obtained from term counts in documents [4].…”

A quasi-current representation for information needs inspired by Two-State Vector Formalism

“…In that book, some notions in IR are translated into analogous notions in QT, such as mapping a document into a state vector, regarding each document as a superposition of words, and replacing the cosine correlation between 10 query and documents with inner product. Beyond that, QT can help address some problems for IR tasks [2,3,4].…”

“…Succeedingly, a series of quantum-based IR (QIR) models were proposed under the mathematical framework of QT, and especially motivated by some 100 quantum phenomena, such as "quantum interference" (QI) and "photon polarization" [4,18,19,20,21]. Zuccon and Azzopardi [4] proposed a Quantum Probability Ranking Principle (QPRP) that implicitly captured dependencies between documents through QI.…”

“…Zuccon and Azzopardi [4] proposed a Quantum Probability Ranking Principle (QPRP) that implicitly captured dependencies between documents through QI. Such a quantum effect was also used for modeling interactions between latent topics [19].…”

Combining Word Semantics within Complex Hilbert Space for Information Retrieval

“…Common to all these proposals is the assumption that information objects (queries, documents, etc.) are represented in real-valued Hilbert spaces, even when the key modelling aspect is the quantum interference phenomenon [3]. Zuccon and Piwowarski argued that this assumption is not imposed by the models themselves, which, being grounded on the mathematics of quantum theory, allow for complex valued representations.…”