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Ranking (information retrieval)

About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.


Papers
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Book ChapterDOI
27 Aug 2009
TL;DR: In this paper, the problem of ranking all process models in a repository according to their similarity with respect to a given process model is investigated, and four graph matching algorithms, ranging from a greedy one to a relatively exhaustive one, are evaluated.
Abstract: We investigate the problem of ranking all process models in a repository according to their similarity with respect to a given process model. We focus specifically on the application of graph matching algorithms to this similarity search problem. Since the corresponding graph matching problem is NP-complete, we seek to find a compromise between computational complexity and quality of the computed ranking. Using a repository of 100 process models, we evaluate four graph matching algorithms, ranging from a greedy one to a relatively exhaustive one. The results show that the mean average precision obtained by a fast greedy algorithm is close to that obtained with the most exhaustive algorithm.

406 citations

Proceedings ArticleDOI
20 Jul 2008
TL;DR: A retrieval model that combines a translation-based language model for the question part with a query likelihood approach for the answer part and incorporates word-to-word translation probabilities learned through exploiting different sources of information is proposed.
Abstract: Retrieval in a question and answer archive involves finding good answers for a user's question. In contrast to typical document retrieval, a retrieval model for this task can exploit question similarity as well as ranking the associated answers. In this paper, we propose a retrieval model that combines a translation-based language model for the question part with a query likelihood approach for the answer part. The proposed model incorporates word-to-word translation probabilities learned through exploiting different sources of information. Experiments show that the proposed translation based language model for the question part outperforms baseline methods significantly. By combining with the query likelihood language model for the answer part, substantial additional effectiveness improvements are obtained.

406 citations

Journal ArticleDOI
TL;DR: A new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking and a semi- supervised long-term RF algorithm to refine the multimedia data representation.
Abstract: We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

405 citations

Proceedings ArticleDOI
01 Jun 1998
TL;DR: An algorithm that detects sub-optimality of a query execution plan during query execution and attempts to correct the problem is described, and it is reported that this can result in significant improvements in the performance of complex queries.
Abstract: For a number of reasons, even the best query optimizers can very often produce sub-optimal query execution plans, leading to a significant degradation of performance. This is especially true in databases used for complex decision support queries and/or object-relational databases. In this paper, we describe an algorithm that detects sub-optimality of a query execution plan during query execution and attempts to correct the problem. The basic idea is to collect statistics at key points during the execution of a complex query. These statistics are then used to optimize the execution of the query, either by improving the resource allocation for that query, or by changing the execution plan for the remainder of the query. To ensure that this does not significantly slow down the normal execution of a query, the Query Optimizer carefully chooses what statistics to collect, when to collect them, and the circumstances under which to re-optimize the query. We describe an implementation of this algorithm in the Paradise Database System, and we report on performance studies, which indicate that this can result in significant improvements in the performance of complex queries.

405 citations

Journal ArticleDOI
TL;DR: This article presents an improved approach to assist diagnosis of failures in software by ranking program statements or blocks in accordance with to how likely they are to be buggy, which out-performs previously proposed methods for the model program, the Siemens test suite and Space.
Abstract: This article presents an improved approach to assist diagnosis of failures in software (fault localisation) by ranking program statements or blocks in accordance with to how likely they are to be buggy. We present a very simple single-bug program to model the problem. By examining different possible execution paths through this model program over a number of test cases, the effectiveness of different proposed spectral ranking methods can be evaluated in idealised conditions. The results are remarkably consistent to those arrived at empirically using the Siemens test suite and Space benchmarks. The model also helps identify groups of metrics that are equivalent for ranking. Due to the simplicity of the model, an optimal ranking method can be devised. This new method out-performs previously proposed methods for the model program, the Siemens test suite and Space. It also helps provide insight into other ranking methods.

405 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168