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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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Proceedings ArticleDOI
23 Aug 2010
TL;DR: This paper proposes a ranking-based framework consisting of a set of binary queries, where each query collects a binary-classification-based comparison result that is fused to predict the age of a person when inferring a person's age.
Abstract: In our daily life, it is much easier to distinguish which person is elder between two persons than how old a person is. When inferring a person's age, we may compare his or her face with many people whose ages are known, resulting in a series of comparative results, and then we conjecture the age based on the comparisons. This process involves numerous pairwise preferences information obtained by a series of queries, where each query compares the target person's face to those faces in a database. In this paper, we propose a ranking-based framework consisting of a set of binary queries. Each query collects a binary-classification-based comparison result. All the query results are then fused to predict the age. Experimental results show that our approach performs better than traditional multi-class-based and regression-based approaches for age estimation.

114 citations

Journal ArticleDOI
TL;DR: The experiments show that the new ranking methods developed here give better ranking performance than both classification based methods in virtual screening and regression methods in QSAR analysis.
Abstract: With chemical libraries increasingly containing millions of compounds or more, there is a fast-growing need for computational methods that can rank or prioritize compounds for screening. Machine learning methods have shown considerable promise for this task; indeed, classification methods such as support vector machines (SVMs), together with their variants, have been used in virtual screening to distinguish active compounds from inactive ones, while regression methods such as partial least-squares (PLS) and support vector regression (SVR) have been used in quantitative structure-activity relationship (QSAR) analysis for predicting biological activities of compounds. Recently, a new class of machine learning methods - namely, ranking methods, which are designed to directly optimize ranking performance - have been developed for ranking tasks such as web search that arise in information retrieval (IR) and other applications. Here we report the application of these new ranking methods in machine learning to the task of ranking chemical structures. Our experiments show that the new ranking methods give better ranking performance than both classification based methods in virtual screening and regression methods in QSAR analysis. We also make some interesting connections between ranking performance measures used in cheminformatics and those used in IR studies.

114 citations

01 Jan 2006
TL;DR: This work presents an approach that adapts the well-known PageRank/HITS algorithms to Semantic Web data and combines ranks from the RDF graph with rank from the context graph, i.e. data sources and their linkage.
Abstract: We present an approach that adapts the well-known PageRank/HITS algorithms to Semantic Web data. Our method combines ranks from the RDF graph with ranks from the context graph, i.e. data sources and their linkage. We present performance evaluation results based on a large RDF data set obtained from the Web.

114 citations

Proceedings ArticleDOI
25 Jul 2004
TL;DR: This work uses meta-data attached to documents in the form of time stamps to measure the distribution of documents retrieved in response to a query, over the time domain, to create a temporal profile for a query and finds that using these temporal features, together with the content of the documents retrieved, can improve the prediction of average precision for a queries.
Abstract: A key missing component in information retrieval systems is self-diagnostic tests to establish whether the system can provide reasonable results for a given query on a document collection. If we can measure properties of a retrieved set of documents which allow us to predict average precision, we can automate the decision of whether to elicit relevance feedback, or modify the retrieval system in other ways. We use meta-data attached to documents in the form of time stamps to measure the distribution of documents retrieved in response to a query, over the time domain, to create a temporal profile for a query. We define some useful features over this temporal profile. We find that using these temporal features, together with the content of the documents retrieved, we can improve the prediction of average precision for a query.

113 citations

Proceedings ArticleDOI
Michael Persin1
01 Aug 1994
TL;DR: The experiments show that the proposed evaluation technique reduces both main memory usage and query evaluation time, based on early recognition of which documents are likely to be highly ranked, without degradation in retrieval effectiveness.
Abstract: Ranking techniques are effective for finding answers in document collections but the cost of evaluation of ranked queries can be unacceptably high. We propose an evaluation technique that reduces both main memory usage and query evaluation time. based on early recognition of which documents are likely to be highly ranked. Our experiments show that, for our test data, the proposed technique evaluates queries in 20% of the time and 2% of the memory taken by the standard inverted file implementation, without degradation in retrieval effectiveness.

113 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