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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.


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Book ChapterDOI
02 Apr 2001
TL;DR: An algorithm is presented to synthesize linear ranking functions that can establish termination of program cycles and the representation of systems of linear inequalities and sets of linear expressions as polyhedral cones allows this search to be reduced to the computation of polars, intersections and projections ofpolyhedral cones.
Abstract: Deductive verification of progress properties relies on finding ranking functions to prove termination of program cycles. We present an algorithm to synthesize linear ranking functions that can establish such termination. Fundamental to our approach is the representation of systems of linear inequalities and sets of linear expressions as polyhedral cones. This representation allows us to reduce the search for linear ranking functions to the computation of polars, intersections and projections of polyhedral cones, problems which have well-known solutions.

237 citations

Book ChapterDOI
31 Aug 2004
TL;DR: A family of approximate top-k algorithms based on probabilistic arguments is introduced and the precision and the efficiency of the developed methods are experimentally evaluated based on a large Web corpus and a structured data collection.
Abstract: Top-k queries based on ranking elements of multidimensional datasets are a fundamental building block for many kinds of information discovery. The best known general-purpose algorithm for evaluating top-k queries is Fagin's threshold algorithm (TA). Since the user's goal behind top-k queries is to identify one or a few relevant and novel data items, it is intriguing to use approximate variants of TA to reduce run-time costs. This paper introduces a family of approximate top-k algorithms based on probabilistic arguments. When scanning index lists of the underlying multidimensional data space in descending order of local scores, various forms of convolution and derived bounds are employed to predict when it is safe, with high probability, to drop candidate items and to prune the index scans. The precision and the efficiency of the developed methods are experimentally evaluated based on a large Web corpus and a structured data collection.

237 citations

Book ChapterDOI
22 Sep 2003
TL;DR: The main objective of this work is to investigate the trade-off between the quality of the induced ranking function and the computational complexity of the algorithm, both depending on the amount of preference information given for each example.
Abstract: We consider supervised learning of a ranking function, which is a mapping from instances to total orders over a set of labels (options). The training information consists of examples with partial (and possibly inconsistent) information about their associated rankings. From these, we induce a ranking function by reducing the original problem to a number of binary classification problems, one for each pair of labels. The main objective of this work is to investigate the trade-off between the quality of the induced ranking function and the computational complexity of the algorithm, both depending on the amount of preference information given for each example. To this end, we present theoretical results on the complexity of pairwise preference learning, and experimentally investigate the predictive performance of our method for different types of preference information, such as top-ranked labels and complete rankings. The domain of this study is the prediction of a rational agent's ranking of actions in an uncertain environment.

237 citations

Proceedings ArticleDOI
01 Sep 2001
TL;DR: It is shown empirically that the score distributions of a number of text search engines on a per query basis may be fitted using an exponential distribution for the set of non-relevant documents and a normal distribution forThe set of relevant documents.
Abstract: In this paper the score distributions of a number of text search engines are modeled. It is shown empirically that the score distributions on a per query basis may be fitted using an exponential distribution for the set of non-relevant documents and a normal distribution for the set of relevant documents. Experiments show that this model fits TREC-3 and TREC-4 data for not only probabilistic search engines like INQUERY but also vector space search engines like SMART for English. We have also used this model to fit the output of other search engines like LSI search engines and search engines indexing other languages like Chinese.It is then shown that given a query for which relevance information is not available, a mixture model consisting of an exponential and a normal distribution can be fitted to the score distribution. These distributions can be used to map the scores of a search engine to probabilities. We also discuss how the shape of the score distributions arise given certain assumptions about word distributions in documents. We hypothesize that all 'good' text search engines operating on any language have similar characteristics.This model has many possible applications. For example, the outputs of different search engines can be combined by averaging the probabilities (optimal if the search engines are independent) or by using the probabilities to select the best engine for each query. Results show that the technique performs as well as the best current combination techniques.

237 citations

Book ChapterDOI
01 Jan 2012
TL;DR: In fuzzy multi-criteria decision-making problems, the ranking of alternatives must take into account their fuzzy scores in all criteria, the weights assigned to each decision criterion, the possible difficulties of comparing two alternatives when one is significantly better than the other on at least one criterion from the complementary subset of criteria, and the decision maker's attitude towards the risk associated with evaluation as mentioned in this paper.
Abstract: In fuzzy Multi-criteria decision-making problems, the ranking of alternatives must take into account their fuzzy scores in all criteria, the weights assigned to each decision criterion, the possible difficulties of comparing two alternatives when one is significantly better than the other on a subset of criteria, but much worse on at least one criterion from the complementary subset of criteria, and the decision maker’s attitude towards the risk associated with evaluation.

236 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