A survey of pre-retrieval query performance predictors

Hauff, Claudia; Hiemstra, Djoerd; Jong, Franciska de

doi:10.1145/1458082.1458311

Cited by 119 publications

(123 citation statements)

References 8 publications

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“…Firstly, we can see that the post-retrieval predictors (bottom half of the table) outperform the pre-retrieval predictors (top half of the table) for this part of the query space. For example, idf max , a high performing predictor in other studies [6], performs poorly at the high end of the query space. This is because users will often choose the same highly discriminating term when creating a query for the same topic.…”

Section: Correlation Of User Extracted Queriesmentioning

confidence: 96%

“…Fundamentally, retrieval predictors can be divided into two classes: pre-retrieval [7,6,12] and post-retrieval [3,10,11] predictors. Pre-retrieval predictors use features from the query, document and collection before a query has been processed in order to ascertain its performance.…”

Section: Background and Related Researchmentioning

confidence: 99%

“…For example, search engines may wish to augment queries in different ways depending on their estimated performance. In fact, if query performance prediction becomes good enough [6], the space of all possible queries for a given topic may be able to be navigated efficiently, so that an initial query can be perturbed effectively. Furthermore, such techniques might be effective for creating good queries when a large number of terms are available.…”

Section: Introductionmentioning

confidence: 99%

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Navigating the User Query Space

Cummins

Lalmas

O’Riordan

et al. 2011

String Processing and Information Retrieval

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Abstract. Query performance prediction (QPP) aims to automatically estimate the performance of a query. Recently there have been many attempts to use these predictors to estimate whether a perturbed version of a query will outperform the original version. In essence, these approaches attempt to navigate the space of queries in a guided manner. In this paper, we perform an analysis of the query space over a substantial number of queries and show that (1) users tend to be able to extract queries that perform in the top 5% of all possible user queries for a specific topic, (2) that post-retrieval predictors outperform preretrieval predictors at the high end of the query space. And, finally (3), we show that some post retrieval predictors are better able to select high performing queries from a group of user queries for the same topic.

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Section: Correlation Of User Extracted Queriesmentioning

confidence: 96%

Section: Background and Related Researchmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Navigating the User Query Space

Cummins

Lalmas

O’Riordan

et al. 2011

String Processing and Information Retrieval

View full text Add to dashboard Cite

show abstract

“…However, since it is a really tedious labour, it cannot be done reliable by a single person. As has been shown recently in Web 2.0 applications, such as YouTube, Yahoo Videos, 7 Metacafe, 8 Revver, 9 and Daily Motion, 10 the community can play an important role to annotate on-line videos, letting multimedia content retrieval based on collaborative social tagging to be extremely successful.…”

Section: Exploiting External Knowledge To Obtain Relevant Visual Exammentioning

confidence: 99%

Exploiting semantics on external resources to gather visual examples for video retrieval

Vallet

Cantador

Jose

2012

Int J Multimed Info Retr

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With the huge and ever rising amount of video content available on the Web, there is a need to facilitate video retrieval functionalities on very large collections. Most of the current Web video retrieval systems rely on manual textual annotations to provide keyword-based search interfaces. These systems have to face the problems that users are often reticent to provide annotations, and that the quality of such annotations is questionable in many cases. An alternative commonly used approach is to ask the user for an image example, and exploit the low-level features of the image to find video content whose keyframes are similar to the image. In this case, the main limitation is the so-called semantic gap, which consists of the fact that low-level image features often do not match with the real semantics of the videos. Moreover, this approach may be a burden to the user, as it requires finding and providing the system with relevant visual examples. Aiming to address this limitation, in this paper, we present a hybrid video retrieval technique that automatically obtains visual examples by performing textual searches on external knowledge sources, such as DBpedia, Flickr and Google Images, which have different coverage and structure characteristics. Our approach exploits the semantics underlying the above knowledge sources to address the semantic gap problem. We have conducted evaluations to assess the quality of visual examples retrieved from the above external knowledge sources.

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“…According to whether or not the retrieval results are used in the prediction, the methods can be classified into pre-retrieval and post-retrieval approaches [10]. The first type has the advantage that the prediction can be taken into account to improve the retrieval process itself.…”

Section: Introductionmentioning

confidence: 99%

Predicting Neighbor Goodness in Collaborative Filtering

Bellogín

Castells

2009

Flexible Query Answering Systems

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract. Performance prediction has gained increasing attention in the IR field since the half of the past decade and has become an established research topic in the field. The present work restates the problem in the subarea of Collaborative Filtering (CF), where it has barely been researched so far. We investigate the adaptation of clarity-based query performance predictors to define predictors of neighbor performance in CF. The proposed predictors are introduced in a memory-based CF algorithm to produce a dynamic variant where neighbor ratings are weighted based on their predicted performance. The approach is tested with encouraging empirical results, as the dynamic variants consistently outperform the baseline algorithms, with increasing difference on small neighborhoods.

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A survey of pre-retrieval query performance predictors

Cited by 119 publications

References 8 publications

Navigating the User Query Space

Navigating the User Query Space

Exploiting semantics on external resources to gather visual examples for video retrieval

Predicting Neighbor Goodness in Collaborative Filtering

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