Search (8 results, page 1 of 1)

Bodoff, D.; Robertson, S.: ¬A new unified probabilistic model (2004) 0.02
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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.; Raban, D.: User models as revealed in web-based research services (2012) 0.01
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Abstract

The user-centered approach to information retrieval emphasizes the importance of a user model in determining what information will be most useful to a particular user, given their context. Mediated search provides an opportunity to elaborate on this idea, as an intermediary's elicitations reveal what aspects of the user model they think are worth inquiring about. However, empirical evidence is divided over whether intermediaries actually work to develop a broadly conceived user model. Our research revisits the issue in a web research services setting, whose characteristics are expected to result in more thorough user modeling on the part of intermediaries. Our empirical study confirms that intermediaries engage in rich user modeling. While intermediaries behave differently across settings, our interpretation is that the underlying user model characteristics that intermediaries inquire about in our setting are applicable to other settings as well.
Bodoff, D.: ¬A re-unification of two competing models for document retrieval (1999) 0.01
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Abstract

2 competing approaches for document retrieval were first identified by Robertson, Maron and Cooper (1982) for probabilistic retrieval. The difficulty of unifying those approaches was introduced as a problem of resolving query-focused with document-focused retrieval, and an approach towards unification was offered. That approach rests on a re-conceptualization of the meaning of terms weight estimates. In this work, we propose a new unified model. The unification problem is re-framed as resulting from a lack of theory regarding the relationship to 2 sorts of data, absolute and relative. This new unified model is valid even for traditional interpretations of term estimates
Bodoff, D.: Relevance for browsing, relevance for searching (2006) 0.01
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Abstract

The concept of relevance has received a great deal of theoretical attention. Separately, the relationship between focused search and browsing has also received extensive theoretical attention. This article aims to integrate these two literatures with a model and an empirical study that relate relevance in focused searching to relevance in browsing. Some factors affect both kinds of relevance in the same direction; others affect them in different ways. In our empirical study, we find that the latter factors dominate, so that there is actually a negative correlation between the probability of a document's relevance to a browsing user and its probability of relevance to a focused searcher.
Bodoff, D.; Wong, S.P.-S.: Documents and queries as random variables : history and implications (2006) 0.01
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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.: Emergence of terminological conventions as a searcher-indexer coordination game (2009) 0.01
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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.; Richter-Levin, Y.: Viewpoints in indexing term assignment (2020) 0.01
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Abstract

The literature on assigned indexing considers three possible viewpoints-the author's viewpoint as evidenced in the title, the users' viewpoint, and the indexer's viewpoint-and asks whether and which of those views should be reflected in an indexer's choice of terms to assign to an item. We study this question empirically, as opposed to normatively. Based on the literature that discusses whose viewpoints should be reflected, we construct a research model that includes those same three viewpoints as factors that might be influencing term assignment in actual practice. In the unique study design that we employ, the records of term assignments made by identified indexers in academic libraries are cross-referenced with the results of a survey that those same indexers completed on political views. Our results indicate that in our setting, variance in term assignment was best explained by indexers' personal political views.

Bodoff, D.; Raban, D.: Question types and intermediary elicitations (2016) 0.00

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Date: 22. 1.2016 11:58:25

Search (8 results, page 1 of 1)

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