Relevance weighting for query independent evidence

Craswell, Nick; Robertson, Stephen; Zaragoza, Hugo; Taylor, Michael

doi:10.1145/1076034.1076106

Cited by 104 publications

(130 citation statements)

References 14 publications

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“…In particular raw biometric scores and various nonlinear transformations of the biometric readings are explored. A particularly promising technique is presented in [5] where a sigmoid functional form is used to transform PageRank, link indegree, ClickDistance and URL length into static scores. This technique forms part of our investigation.…”

Section: Towards Static Biometric Scoresmentioning

confidence: 99%

Advances in Information Retrieval

Ruotsalo¹,

Hyvönen²

2010

Lecture Notes in Computer Science

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Abstract. Personal lifelog archives contain digital records captured from an individual's daily life, e.g. emails, web pages downloaded and SMSs sent or received. While capturing this information is becoming increasingly easy, subsequently locating relevant items in response to user queries from within these archives is a significant challenge. This paper presents a novel query independent static biometric scoring approach for re-ranking result lists retrieved from a lifelog using a BM25 model for content and content + context data. For this study we explored the utility of galvanic skin response (GSR) and skin temperature (ST) associated with past experience of items as a measure of potential future significance of items. Results obtained indicate that our static scoring techniques are useful in re-ranking retrieved result lists.

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Section: Towards Static Biometric Scoresmentioning

confidence: 99%

Advances in Information Retrieval

Ruotsalo¹,

Hyvönen²

2010

Lecture Notes in Computer Science

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“…using the term frequency in specific fields of structured documents (e.g. title, abstract) [11], or integrating query-independent evidence in the retrieval model in the form of prior probabilities for a document [3,6] ('prior' because they are known before the query). In short, when determining the relevance between a query and a document, most IR models use primarily query-dependent term statistics, and sometimes also add query-independent evidence to further enhance retrieval performance.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, priors are combined with the retrieval model either using heuristics or handtuned parameters [3]. In this work, we combine our proposed priors with the LM in two different ways: using a standard logarithmic combination, and proposing a novel combination that considers the prior as a measure of the risk of accepting the score given by the query likelihood estimation of the LM.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Document Length Priors for Language Models

Blanco

Barreiro

Lecture Notes in Computer Science

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Abstract. This paper addresses the issue of devising a new document prior for the language modeling (LM) approach for Information Retrieval. The prior is based on term statistics, derived in a probabilistic fashion and portrays a novel way of considering document length. Furthermore, we developed a new way of combining document length priors with the query likelihood estimation based on the risk of accepting the latter as a score. This prior has been combined with a document retrieval language model that uses Jelinek-Mercer (JM), a smoothing technique which does not take into account document length. The combination of the prior boosts the retrieval performance, so that it outperforms a LM with a document length dependent smoothing component (Dirichlet prior) and other state of the art high-performing scoring function (BM25). Improvements are significant, robust across different collections and query sizes.

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“…For example, it proved non-trivial to include query-independent features such as PageRank and inlink count into the BM25 ranking formula [6]. Therefore, a model that can handle arbitrary query-dependent and query-independent features is desirable.…”

Section: Introductionmentioning

confidence: 99%

Linear feature-based models for information retrieval

2007

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Abstract. There have been a number of linear, feature-based models proposed by the information retrieval community recently. Although each model is presented differently, they all share a common underlying framework. In this paper we explore and discuss the theoretical issues of this framework, including a novel look at the parameter space. We then detail supervised training algorithms that directly maximize the evaluation metric under consideration, such as mean average precision. We present results that show training models in this way can lead to significantly better test set performance compared to other training methods that do not directly maximize the metric. Finally, we show that linear feature-based models can consistently and significantly outperform current state of the art retrieval models with the correct choice of features.

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Relevance weighting for query independent evidence

Cited by 104 publications

References 14 publications

Advances in Information Retrieval

Advances in Information Retrieval

Probabilistic Document Length Priors for Language Models

Linear feature-based models for information retrieval

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