Processing Queries in Session in a Quantum-Inspired IR Framework

Frommholz, Ingo; Piwowarski, Benjamin; Lalmas, Mounia; Rijsbergen, Keith van

doi:10.1007/978-3-642-20161-5_86

Cited by 7 publications

(7 citation statements)

References 4 publications

(7 reference statements)

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“…Our idea is inspired by the view of an IR system as analogous to a quantum system used in physics. In this section, we overview the mathematical formalism [34] behind the quantum-theoretic models [8,28] of IR. In contrast to set-based classical probability Theory, in quantum probabilities, the probabilistic space is geometrically defined, and its representation becomes an infinite set of angles and distances in a finite or infinite dimensional Hilbert Space [32] denoted by H. A vector space with complete inner product forms a Hilbert space.…”

Section: Mathematical Preliminariesmentioning

confidence: 99%

Semantic Hilbert Space for Interactive Image Retrieval

Jaiswal

Liu

Frommholz

2021

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval

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The paper introduces a model for interactive image retrieval utilising the geometrical framework of information retrieval (IR). We tackle the problem of image retrieval based on an expressive user information need in form of a textual-visual query, where a user is attempting to find an image similar to the picture in their mind during querying. The user information need is expressed using guided visual feedback based on Information Foraging which lets the user perception embed within the model via semantic Hilbert space (SHS). This framework is based on the mathematical formalism of quantum probabilities and aims to understand the relationship between user textual and image input, where the image in the input is considered a form of visual feedback. We propose SHS, a quantum-inspired approach where the textual-visual query is regarded analogously to a physical system that allows for modelling different system states and their dynamic changes thereof based on observations (such as queries, relevance judgements). We will be able to learn the input multimodal representation and relationships between textual-image queries for retrieving images. Our experiments are conducted on the MIT States and Fashion200k datasets that demonstrate the effectiveness of finding particular images autocratically when the user inputs are semantically expressive.

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Section: Mathematical Preliminariesmentioning

confidence: 99%

Semantic Hilbert Space for Interactive Image Retrieval

Jaiswal

Liu

Frommholz

2021

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval

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“…In [Frommholz et al 2011;Piwowarski and Lalmas 2009a], a general framework for query reformulation using Quantum probabilities is described. The queries are represented as density matrices in a term space and query reformulation updates the query density matrix, which can be used to detect change in user IN in a search session.…”

Section: User Interactionsmentioning

confidence: 99%

“…Projection Models [van Rijsbergen 2004], [Melucci 2005a], [Melucci 2005b], [Piwowarski and Lalmas 2009a], [Piwowarski et al 2008], [Piwowarski et al 2010a], [Piwowarski et al 2010c], [Piwowarski et al 2010b], [Caputo et al 2011], ] Quantum Language Models [Sordoni et al 2013b], [Xie et al 2015], [Zhang et al 2018d] [Hou and Song 2009], [Hou et al 2013], [Zhang et al 2018b], [Zhang et al 2018a], [Zhang et al 2018d] , [Blacoe et al 2013], [Blacoe 2015], [Basile and Tamburini 2017], [Li et al 2018a] Quantum-inspired Ranking [Zuccon et al 2009], , , Multimodal IR [Wang et al 2010a], [Kaliciak et al 2011], [Kaliciak et al 2013], [Gkoumas et al 2018], [Zhang et al 2018c], ] Quantum-inspired Representation Learning [Aerts and Czachor 2004], [Bruza and Cole 2005], [Sordoni et al 2013a], [Zuccon et al 2011], [Wittek et al 2014], ], [Jaiswal et al 2018, , , [Buccio et al 2018], User Interactions Projection Models [Melucci and White 2007], [Melucci 2008], [Piwowarski and Lalmas 2009a], [Frommholz et al 2011], , , [Wang et al 2017…”

Section: Representation and Rankingmentioning

confidence: 99%

A Survey of Quantum Theory Inspired Approaches to Information Retrieval

2020

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Since 2004, researchers have been using the mathematical framework of Quantum Theory (QT) in Information Retrieval (IR). QT offers a generalized probability and logic framework. Such a framework has been shown capable of unifying the representation, ranking and user cognitive aspects of IR, and helpful in developing more dynamic, adaptive and context-aware IR systems. Although Quantum-inspired IR is still a growing area, a wide array of work in different aspects of IR has been done and produced promising results. This paper presents a survey of the research done in this area, aiming to show the landscape of the field and draw a road-map of future directions.

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“…Where IDF = log − +0.5 +0.5 , = 1 (1 − + ), 3 is between 7 and 1,000 (usually, qtf = 1 and 3 does not impact), qtf is the frequency of the term in the query.…”

Section: Best Match No 25mentioning

confidence: 99%

Improving Information Retraival in Search Engine Using and Positive and Negative Relavance Feedback

2017

IJRTER

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Abstract:The relative ineffectiveness of information retrieval systems is largely caused by the inaccuracy with which a query formed by a few keywords models the actual user information need. One well known method to overcome this limitation is automatic query expansion (AQE), whereby the user's original query is augmented by new features with a similar meaning. Information Retrieval (IR) is concerned with indexing and retrieving documents including information relevant to a user's information need. Relevance Feedback (RF) is a class of effective algorithms for improving Information Retrieval (IR) and it consists of gathering further data representing the user's information need and automatically creating a new query. In this paper, we propose a class of RF algorithms inspired by quantum detection to re-weight the query terms and to re-rank the document retrieved by an IR system. These algorithms project the query vector on a subspace spanned by the eigenvector which maximizes the distance between the distribution of quantum probability of relevance and the distribution of quantum probability of non-relevance. The experiments showed that the RF algorithms inspired by quantum detection can outperform the state-of-the-art algorithms.

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Processing Queries in Session in a Quantum-Inspired IR Framework

Cited by 7 publications

References 4 publications

Semantic Hilbert Space for Interactive Image Retrieval

Semantic Hilbert Space for Interactive Image Retrieval

A Survey of Quantum Theory Inspired Approaches to Information Retrieval

Improving Information Retraival in Search Engine Using and Positive and Negative Relavance Feedback

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