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Bodoff, D.; Enache, D.; Kambil, A.; Simon, G.; Yukhimets, A.: ¬A unified maximum likelihood approach to document retrieval (2001) 0.00

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Abstract: Empirical work shows significant benefits from using relevance feedback data to improve information retrieval (IR) performance. Still, one fundamental difficulty has limited the ability to fully exploit this valuable data. The problem is that it is not clear whether the relevance feedback data should be used to train the system about what the users really mean, or about what the documents really mean. In this paper, we resolve the question using a maximum likelihood framework. We show how all the available data can be used to simultaneously estimate both documents and queries in proportions that are optimal in a maximum likelihood sense. The resulting algorithm is directly applicable to many approaches to IR, and the unified framework can help explain previously reported results as well as guidethe search for new methods that utilize feedback data in IR

Bodoff, D.: Emergence of terminological conventions as a searcher-indexer coordination game (2009) 0.00
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Abstract

In the traditional model of information retrieval, searchers and indexers choose query and index terms, respectively, and these term choices are ultimately compared in a matching process. One of the main challenges in information science and information retrieval is that searchers and indexers often do not choose the same term even though the item is relevant to the need whereas at other times they do choose the same term even though it is not relevant. But if both searchers and indexers have the opportunity to review feedback data showing the success or failure of their previous term choices, then there exists an evolutionary force that, all else being equal, will lead to helpful convergence in searchers' and indexers' term usage when the information is relevant, and helpful divergence of term usage when it is not. Based on learning theory, and new theory presented here, it is possible to predict which terms will emerge as the terminological conventions that are used by groups of searchers and the indexers of relevant and nonrelevant information items.
Bodoff, D.; Wu, B.; Wong, K.Y.M.: Relevance data for language models using maximum likelihood (2003) 0.00
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Abstract

We present a preliminary empirical test of a maximum likelihood approach to using relevance data for training information retrieval (IR) parameters. Similar to language models, our method uses explicitly hypothesized distributions for documents and queries, but we add to this an explicitly hypothesized distribution for relevance judgments. The method unifies document-oriented and query-oriented views. Performance is better than the Rocchio heuristic for document and/or query modification. The maximum likelihood methodology also motivates a heuristic estimate of the MLE optimization. The method can be used to test competing hypotheses regarding the processes of authors' term selection, searchers' term selection, and assessors' relevancy judgments.
Bodoff, D.; Robertson, S.: ¬A new unified probabilistic model (2004) 0.00
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Abstract

This paper proposes a new unified probabilistic model. Two previous models, Robertson et al.'s "Model 0" and "Model 3," each have strengths and weaknesses. The strength of Model 0 not found in Model 3, is that it does not require relevance data about the particular document or query, and, related to that, its probability estimates are straightforward. The strength of Model 3 not found in Model 0 is that it can utilize feedback information about the particular document and query in question. In this paper we introduce a new unified probabilistic model that combines these strengths: the expression of its probabilities is straightforward, it does not require that data must be available for the particular document or query in question, but it can utilize such specific data if it is available. The model is one way to resolve the difficulty of combining two marginal views in probabilistic retrieval.
Bodoff, D.; Wong, S.P.-S.: Documents and queries as random variables : history and implications (2006) 0.00
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Abstract

The view of documents and/or queries as random variables is gaining importance in the theory of information retrieval. We argue that traditional probabilistic models consider documents and queries as random variables, but that newer models such as language modeling and our unified model take this one step further. The additional step is called error in predictors. Such models consider that we don't observe the document and query random variables that are modeled to predict relevance probabilistically. Rather, there are additional random variables, which are the observed documents and queries. We discuss some important implications of this idea for parameter estimation, relevance prediction, and even test-collection construction. By clarifying the positions of various probabilistic models on this question, and presenting in one place many of its implications, this article aims to deepen our common understanding of the theories behind traditional probabilistic models, and to strengthen the theoretical basis for further development of more recent approaches such as language modeling.
Bodoff, D.: Test theory for evaluating reliability of IR test collections (2008) 0.00
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Abstract

Classical test theory offers theoretically derived reliability measures such as Cronbach's alpha, which can be applied to measure the reliability of a set of Information Retrieval test results. The theory also supports item analysis, which identifies queries that are hampering the test's reliability, and which may be candidates for refinement or removal. A generalization of Classical Test Theory, called Generalizability Theory, provides an even richer set of tools. It allows us to estimate the reliability of a test as a function of the number of queries, assessors (relevance judges), and other aspects of the test's design. One novel aspect of Generalizability Theory is that it allows this estimation of reliability even before the test collection exists, based purely on the numbers of queries and assessors that it will contain. These calculations can help test designers in advance, by allowing them to compare the reliability of test designs with various numbers of queries and relevance assessors, and to spend their limited budgets on a design that maximizes reliability. Empirical analysis shows that in cases for which our data is representative, having more queries is more helpful for reliability than having more assessors. It also suggests that reliability may be improved with a per-document performance measure, as opposed to a document-set based performance measure, where appropriate. The theory also clarifies the implicit debate in IR literature regarding the nature of error in relevance judgments.

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