Mathematical Foundations of Information Retrieval

Dominich, Sándor

doi:10.1162/coli.2000.28.1.103

Cited by 21 publications

(10 citation statements)

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“…In a similarity-based retrieval model (Dominich, 2000(Dominich, , 2001, it is assumed that the relevance status of a document with respect to a query is correlated with the similarity between the query and the document at some level of representation; the more similar to a query a document is, the more relevant the document is assumed to be. In practice, we can use any similarity measure that preserves such correlation to generate a relevance status value (RSV) for each document and rank documents accordingly.…”

Section: Similarity-based Modelsmentioning

confidence: 99%

“…The decision problem is a more general one where the action space, in principle, consists of all the possible actions that the system can take in response to a query. The scope of the decision space is a significant departure from existing decision-theoretic treatment of retrieval (Wong et al, 1991;Dominich, 2001). Such a general decision-theoretic view explicitly suggests that retrieval is modeled as an interactive process that involves cycles of a user reformulating the query and the system presenting information.…”

Section: A Decision-theoretic View Of Retrievalmentioning

confidence: 99%

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A risk minimization framework for information retrieval

Zhai

Lafferty

2006

Information Processing & Management

120

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Section: Similarity-based Modelsmentioning

confidence: 99%

Section: A Decision-theoretic View Of Retrievalmentioning

confidence: 99%

A risk minimization framework for information retrieval

Zhai

Lafferty

2006

Information Processing & Management

120

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“…At the same time, since Ref. [49] proposed to treat precision and recall as complementary operators regulating the surface of effectiveness in information retrieval, whereas Ref. [50] argued that relevance is an operator on Hilbert space and as such is part of the quantum measurement process, neither was our insight totally unexpected.…”

Section: Information Seeking Is Quantum-likementioning

confidence: 89%

Risk and Ambiguity in Information Seeking: Eye Gaze Patterns Reveal Contextual Behavior in Dealing with Uncertainty

et al. 2016

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Information foraging connects optimal foraging theory in ecology with how humans search for information. The theory suggests that, following an information scent, the information seeker must optimize the tradeoff between exploration by repeated steps in the search space vs. exploitation, using the resources encountered. We conjecture that this tradeoff characterizes how a user deals with uncertainty and its two aspects, risk and ambiguity in economic theory. Risk is related to the perceived quality of the actually visited patch of information, and can be reduced by exploiting and understanding the patch to a better extent. Ambiguity, on the other hand, is the opportunity cost of having higher quality patches elsewhere in the search space. The aforementioned tradeoff depends on many attributes, including traits of the user: at the two extreme ends of the spectrum, analytic and wholistic searchers employ entirely different strategies. The former type focuses on exploitation first, interspersed with bouts of exploration, whereas the latter type prefers to explore the search space first and consume later. Our findings from an eye-tracking study of experts' interactions with novel search interfaces in the biomedical domain suggest that user traits of cognitive styles and perceived search task difficulty are significantly correlated with eye gaze and search behavior. We also demonstrate that perceived risk shifts the balance between exploration and exploitation in either type of users, tilting it against vs. in favor of ambiguity minimization. Since the pattern of behavior in information foraging is quintessentially sequential, risk and ambiguity minimization cannot happen simultaneously, leading to a fundamental limit on how good such a tradeoff can be. This in turn connects information seeking with the emergent field of quantum decision theory.

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“…The idea of describing the data in terms of abstract Hilbert spaces has been used (in the context of database search) in [2,12,14,29,34].…”

Section: Connections To Previous Workmentioning

confidence: 99%

Semantic Distillation: A Method for Clustering Objects by their Contextual Specificity

Sierocinski¹,

Béchec²,

Théret³

et al. 2008

Nature Inspired Cooperative Strategies for Optimization (NICSO 2007)

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Techniques for data-mining, latent semantic analysis, contextual search of databases, etc. have long ago been developed by computer scientists working on information retrieval (IR). Experimental scientists, from all disciplines, having to analyse large collections of raw experimental data (astronomical, physical, biological, etc.) have developed powerful methods for their statistical analysis and for clustering, categorising, and classifying objects. Finally, physicists have developed a theory of quantum measurement, unifying the logical, algebraic, and probabilistic aspects of queries into a single formalism.The purpose of this paper is twofold: first to show that when formulated at an abstract level, problems from IR, from statistical data analysis, and from physical measurement theories are very similar and hence can profitably be cross-fertilised, and, secondly, to propose a novel method of fuzzy hierarchical clustering, termed semantic distillation -strongly inspired from the theory of quantum measurement -, we developed to analyse raw data coming from various types of experiments on DNA arrays. We illustrate the method by analysing DNA arrays experiments and clustering the genes of the array according to their specificity.

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Mathematical Foundations of Information Retrieval

Cited by 21 publications

References 0 publications

A risk minimization framework for information retrieval

A risk minimization framework for information retrieval

Risk and Ambiguity in Information Seeking: Eye Gaze Patterns Reveal Contextual Behavior in Dealing with Uncertainty

Semantic Distillation: A Method for Clustering Objects by their Contextual Specificity

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