NDLI: Modeling Interactive Information Retrieval as a Stochastic Process

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Modeling Interactive Information Retrieval as a Stochastic Process

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Modeling Interactive Information Retrieval as a Stochastic Process

Content Provider	ACM Digital Library
Author	Fuhr, Norbert
Abstract	Stochastic models have a long history in IR, starting from probabilistic models for (non-interactive) IR, and later for evaluation measures like e.g. average precision or discounted cumulated gain. The probabilistic ranking principle defines the order in which a user should inspect the documents retrieved by the system, for maximizing the retrieval quality experienced by the user. In a similar way, for interactive IR, the interactive probabilistic ranking principle (IPRP) defines a framework for the optimum ordering of suggestions / possible actions to be considered by the user. For modeling sequences of interactions, different variants of Markov models have been proposed by a number of researchers. Here a user moves stochastically between different model states, which are not directly observable; instead, some signal is emitted along with each transition. In the simple Markov model, there is a unique mapping from signal sequences to state sequences, while hidden Markov models drop this assumption. In partially observable Markov models, some transitions may not be observable at all. Orthogonal to this distinction, in Markov decision processes, it is additionally assumed that the user achieves a certain gain with each transition. Although the theoretic foundations of these models are well-known, their application to interactive retrieval raises a number of research questions. In the ideal case, we would like to model search progress at a level that is comparable to cognitive models. Thus, as a basic requirement, the states should have some semantic meaning and correspond to different cognitive tasks / states of the user. Moreover, search progress should be somehow reflected by the sequence of states the user passes through. As basic "signals" we have the user's actions which are typically recorded in the system log, and thus easily available for experimentation. Furthermore, other observation data can be collected, such as key logging, mouse movements, eye tracking or EEG data (the latter two types only in lab experiments). There are three major applications of these models. First, and most important, as a realization of the IPRP, they can be used for user guidance. Second, the analysis of these models serves for a detailed, user-oriented analysis of a interactive IR system, pointing out e.g. where users spend most of their time, or where the transition probabilities of specific actions is below the expectation. Third, the models can be used for more realistic stochastic simulations of interactive IR. In this talk, after illustrating the basic concepts, we present some recent research results illustrating the general feasibility of research along these lines.
Starting Page	1
Ending Page	1
Page Count	1
File Format	PDF
ISBN	9781450346771
DOI	10.1145/3020165.3020190
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-03-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	User guidance Search log analysis Markov models
Content Type	Text
Resource Type	Article

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