A risk minimization framework for information retrieval

Zhai, ChengXiang; Lafferty, John

doi:10.1016/j.ipm.2004.11.003

Cited by 120 publications

(62 citation statements)

References 52 publications

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“…4 to the following four popular language models: the query likelihood model by Ponte and Croft [1998], the language model by Hiemstra [2001], which we refer to as Hiemstra's model, the risk minimization model by Zhai and Lafferty [2006], and the relevance model by Lavrenko and Croft [2003]. Note that we focus here on the probabilistic aspects of the mentioned models because their more conceptual aspects are discussed in other work, for example the one mentioned above.…”

Section: Language Modelsmentioning

confidence: 99%

“…Zhai and Lafferty [2006] propose the risk-minimization model that considers drawing a single term (the sample space is T 1 ) from a query language model and from the language model of each document. The model ranks a document d by the Kullback-Leibner (KL) divergence between the two distributions:…”

Section: Hiemstra's Modelmentioning

confidence: 99%

“…We find that not all PR models, even popular ones, can be mapped onto one of the PRP's. As the second type of models, we consider four variations of language models: the query likelihood model [Ponte and Croft, 1998], the language model by Hiemstra [2001] (referred to as Hiemstra's model ), the risk-minimization model [Zhai and Lafferty, 2006], and the relevance model [Lavrenko and Croft, 2003]. These models are commonly thought to implement the PRP by being comparable to PR models.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic models in IR and their relationships

2013

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A solid research path towards new information retrieval (IR) models is to further develop the theory behind existing models. A profound understanding of these models is therefore essential. In this paper, we revisit Probability Ranking Principle (PRP)-based models, Probability of Relevance (PR) models, and language models, finding conceptual differences in their definition and interrelationships. The probabilistic model of the PRP has not been explicitly defined previously, but doing so leads to the formulation of two actual principles with different objectives. First, the belief probability ranking principle (BPRP), which considers uncertain relevance between known documents and the current query, and second, the popularity probability ranking principle (PPRP), which considers the probability of relevance of documents among multiple queries with the same features. Our analysis shows how some of the discussed PR models implement the BPRP or the PPRP while others do not. However, for some models the parameter estimation is challenging. Finally, language models are often presented as related to PR models. However, we find that language models differ from PR models in every aspect of a probabilistic model and the effectiveness of language models cannot be explained by the PRP.⋆ This paper is an extended version of Aly and Demeester [2011], with the following additional contributions: (1) the finding that there are actually two distinct PRPs, (2) the investigation of three additional models from the unified framework of PR models, and (3) a more elaborate discussion of related work. The final publication is available at

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Section: Language Modelsmentioning

confidence: 99%

Section: Hiemstra's Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic models in IR and their relationships

2013

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“…Hiemstra's model ranks a document by the probability that the user had document d in mind given the observed query terms, P H (D ′ =d|T =qt). The risk-minimization model [20] considers the process of drawing a single term (sample space T 1 ) from a query language model and from the language model of each document. Documents are ranked by the Kullback-Leibner divergence between the distribution of the query language model and the document's language model.…”

Section: Language Modelsmentioning

confidence: 99%

Towards a Better Understanding of the Relationship between Probabilistic Models in IR

Aly

Demeester

2011

Lecture Notes in Computer Science

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Abstract. Probability of relevance (PR) models are generally assumed to implement the Probability Ranking Principle (PRP) of IR, and recent publications claim that PR models and language models are similar. However, a careful analysis reveals two gaps in the chain of reasoning behind this statement. First, the PRP considers the relevance of particular documents, whereas PR models consider the relevance of any query-document pair. Second, unlike PR models, language models consider draws of terms and documents. We bridge the first gap by showing how the probability measure of PR models can be used to define the probabilistic model of the PRP. Furthermore, we argue that given the differences between PR models and language models, the second gap cannot be bridged at the probabilistic model level. We instead define a new PR model based on logistic regression, which has a similar score function to the one of the query likelihood model. The performance of both models is strongly correlated, hence providing a bridge for the second gap at the functional and ranking level. Understanding language models in relation with logistic regression models opens ample new research directions which we propose as future work.

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“…Using user session information has already been studied in IR. For example, Zhai [5] proposes a risk minimisation framework where each interaction between a user and the system can be captured by a profile described as a language model. However, this type of profile-based approach does not exhibit how to actually take into account query reformulation.…”

Section: Introductionmentioning

confidence: 99%

Processing Queries in Session in a Quantum-Inspired IR Framework

Frommholz

Piwowarski

Lalmas

et al. 2011

Lecture Notes in Computer Science

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Abstract. In a search session, users tend to reformulate their queries, for instance because they want to generalise or specify them, or because they are undergoing a drift in their information need. This motivates to regard queries not in isolation, but within the session they are embedded in. In this poster, we propose an approach inspired by quantum mechanics to represent queries and their reformulations as density operators. Differently constructed densities can potentially be applied for different types of query reformulation. To do so, we propose and discuss indicators that can hint us to the type of query reformulation we are dealing with.

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A risk minimization framework for information retrieval

Cited by 120 publications

References 52 publications

Probabilistic models in IR and their relationships

Probabilistic models in IR and their relationships

Towards a Better Understanding of the Relationship between Probabilistic Models in IR

Processing Queries in Session in a Quantum-Inspired IR Framework

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