Search (3 results, page 1 of 1)

  • × author_ss:"Bodoff, D."
  • × theme_ss:"Retrievalalgorithmen"
  1. 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
    Type
    a
  2. 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.
    Type
    a
  3. Bodoff, D.; Wong, S.P.-S.: Documents and queries as random variables : history and implications (2006) 0.00 
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    Type
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