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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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Proceedings ArticleDOI
06 Nov 2006
TL;DR: This paper introduces the notion of ranking robustness, which refers to a property of a ranked list of documents that indicates how stable the ranking is in the presence of uncertainty in the ranked documents and proposes a statistical measure called the robustness score to quantify this notion.
Abstract: In this paper, we introduce the notion of ranking robustness, which refers to a property of a ranked list of documents that indicates how stable the ranking is in the presence of uncertainty in the ranked documents. We propose a statistical measure called the robustness score to quantify this notion. We demonstrate that the robustness score significantly and consistently correlates with query performance in a variety of TREC test collections including the GOV2 collection. We compare the robustness score with the clarity score method which is the state-of-the-art technique for query performance prediction. Our experimental results show that the robustness score performs better than or at least as good as the clarity score. We find that the clarity score is barely correlated with query performance on the GOV2 collection while the correlation between the robustness score and query performance remains significant. We also notice that a combination of the two usually results in more prediction power.

147 citations

Proceedings ArticleDOI
24 Jul 2011
TL;DR: The original manifold ranking algorithm is extended and a new framework named Efficient Manifold Ranking (EMR) is proposed, which significantly reduces the computational time and makes it a promising method to large scale real world retrieval problems.
Abstract: Manifold Ranking (MR), a graph-based ranking algorithm, has been widely applied in information retrieval and shown to have excellent performance and feasibility on a variety of data types. Particularly, it has been successfully applied to content-based image retrieval, because of its outstanding ability to discover underlying geometrical structure of the given image database. However, manifold ranking is computationally very expensive, both in graph construction and ranking computation stages, which significantly limits its applicability to very large data sets. In this paper, we extend the original manifold ranking algorithm and propose a new framework named Efficient Manifold Ranking (EMR). We aim to address the shortcomings of MR from two perspectives: scalable graph construction and efficient computation. Specifically, we build an anchor graph on the data set instead of the traditional k-nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking computation. The experimental results on a real world image database demonstrate the effectiveness and efficiency of our proposed method. With a comparable performance to the original manifold ranking, our method significantly reduces the computational time, makes it a promising method to large scale real world retrieval problems.

146 citations

Proceedings ArticleDOI
13 Jun 2004
TL;DR: A rank-aware query optimization framework that fully integrates rank-join operators into relational query engines is introduced based on extending the System R dynamic programming algorithm in both enumeration and pruning and introduces a probabilistic model for estimating the input cardinality, and hence the cost of a rank- join operator.
Abstract: Ranking is an important property that needs to be fully supported by current relational query engines. Recently, several rank-join query operators have been proposed based on rank aggregation algorithms. Rank-join operators progressively rank the join results while performing the join operation. The new operators have a direct impact on traditional query processing and optimization.We introduce a rank-aware query optimization framework that fully integrates rank-join operators into relational query engines. The framework is based on extending the System R dynamic programming algorithm in both enumeration and pruning. We define ranking as an interesting property that triggers the generation of rank-aware query plans. Unlike traditional join operators, optimizing for rank-join operators depends on estimating the input cardinality of these operators. We introduce a probabilistic model for estimating the input cardinality, and hence the cost of a rank-join operator. To our knowledge, this paper is the first effort in estimating the needed input size for optimal rank aggregation algorithms. Costing ranking plans, although challenging, is key to the full integration of rank-join operators in real-world query processing engines. We experimentally evaluate our framework by modifying the query optimizer of an open-source database management system. The experiments show the validity of our framework and the accuracy of the proposed estimation model.

146 citations

Proceedings ArticleDOI
01 Sep 2017
TL;DR: An argument search framework for studying how people query for arguments, how to mine arguments from the web, or how to rank them is developed and a prototype search engine is built that relies on an initial, freely accessible index of nearly 300k arguments crawled from reliable web resources.
Abstract: Computational argumentation is expected to play a critical role in the future of web search. To make this happen, many search-related questions must be revisited, such as how people query for arguments, how to mine arguments from the web, or how to rank them. In this paper, we develop an argument search framework for studying these and further questions. The framework allows for the composition of approaches to acquiring, mining, assessing, indexing, querying, retrieving, ranking, and presenting arguments while relying on standard infrastructure and interfaces. Based on the framework, we build a prototype search engine, called args, that relies on an initial, freely accessible index of nearly 300k arguments crawled from reliable web resources. The framework and the argument search engine are intended as an environment for collaborative research on computational argumentation and its practical evaluation.

146 citations

Patent
28 Jun 2006
TL;DR: In this article, the authors present an architecture for the creation and processing of reputation data for entities such as websites, users, hardware, software, documents, objects and facts, such that the reputation of websites provides a metric in connection with ranking of search results as well as enhancing delivery of meaningful and accurate information to users.
Abstract: Architecture for creation and processing of reputation data for entities such as websites, users, hardware, software, documents, objects and facts. Reputation data can be utilized in connection with web-based searching such that the reputation of websites provides a metric in connection with ranking of search results as well as enhancing delivery of meaningful and accurate information to users. A computer-implemented system is provided that comprises an aggregation component for receiving and aggregating information relating to an entity (e.g., user, website, data, hardware, software), and a reputation engine that employs the aggregated information to generate reputation data therefrom. Other aspects allow for management of the data, hardware and software based on the reputation data, and access to such entities.

146 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