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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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Patent
08 Apr 1997
TL;DR: In this article, a method and system for assisting a user in solving a new problem case within a selected domain, such as a complex apparatus, is presented. But the method comprises the steps of providing a case database comprising domain knowledge for the selected domain and previously solved cases, each previously solved case including a plurality of case attributes, said case attributes comprising case attribute names and associated values.
Abstract: A method and system for assisting a user in solving a new problem case within a selected domain, such as a complex apparatus. The method comprises the steps of providing a case database comprising domain knowledge for the selected domain and previously solved cases, each previously solved case including a plurality of case attributes, said case attributes comprising case attribute names and associated values, prompting the user to select from the case attributes a set of new problem case attributes considered to be relevant to the new problem case and to provide current values for each of the new problem case attributes, searching the database of solved cases for candidate solved cases that have one or more of the new problem case attributes selected by the user and generating a list of said candidate solved cases, matching the candidate solved cases to the new problem case by comparing the value for each of the case attributes in the new problem case to the value for the same case attribute in each of the candidate solved cases, ranking the candidate solved cases in descending order of similarity and presenting a list of candidate solved cases in order of relevance based upon the ranking, generating additional questions based upon unanswered attributes of the candidate solved cases for which values have not yet been provided by the user, to assist the user to select and provide values for the unanswered attributes and thereby appropriately order the candidate solved cases; and repeating the above steps until the user is satisfied with the list of candidate solved cases.

236 citations

Proceedings Article
06 Jan 2007
TL;DR: A novel extractive approach based on manifold-ranking of sentences to this summarization task can significantly outperform existing approaches of the top performing systems in DUC tasks and baseline approaches.
Abstract: Topic-focused multi-document summarization aims to produce a summary biased to a given topic or user profile. This paper presents a novel extractive approach based on manifold-ranking of sentences to this summarization task. The manifold-ranking process can naturally make full use of both the relationships among all the sentences in the documents and the relationships between the given topic and the sentences. The ranking score is obtained for each sentence in the manifold-ranking process to denote the biased information richness of the sentence. Then the greedy algorithm is employed to impose diversity penalty on each sentence. The summary is produced by choosing the sentences with both high biased information richness and high information novelty. Experiments on DUC2003 and DUC2005 are performed and the ROUGE evaluation results show that the proposed approach can significantly outperform existing approaches of the top performing systems in DUC tasks and baseline approaches.

236 citations

Proceedings ArticleDOI
15 Aug 2005
TL;DR: Novel learning methods for estimating the quality of results returned by a search engine in response to a query and the usefulness of quality estimation for several applications, among them improvement of retrieval, detecting queries for which no relevant content exists in the document collection, and distributed information retrieval are presented.
Abstract: In this article we present novel learning methods for estimating the quality of results returned by a search engine in response to a query. Estimation is based on the agreement between the top results of the full query and the top results of its sub-queries. We demonstrate the usefulness of quality estimation for several applications, among them improvement of retrieval, detecting queries for which no relevant content exists in the document collection, and distributed information retrieval. Experiments on TREC data demonstrate the robustness and the effectiveness of our learning algorithms.

236 citations

Proceedings ArticleDOI
09 Mar 2003
TL;DR: It is shown that the rank based method, named Borda Count, is competitive with score based methods, but this is not true for metasearch, and it will be shown that Markov chain based methods compete with Score based methods.
Abstract: Given a set of rankings, the task of ranking fusion is the problem of combining these lists in such a way to optimize the performance of the combination. The ranking fusion problem is encountered in many situations and, e.g., metasearch is a prominent one. It deals with the problem of combining the result lists returned by multiple search engines in response to a given query, where each item in a result list is ordered with respect to a search engine and a relevance score. Several ranking fusion methods have been proposed in the literature. They can be classified based on whether: (i) they rely on the rank; (ii) they rely on the score; and (iii) they require training data or not. Our paper will make the following contributions: (i) we will report experimental results for the Markov chain rank based methods, for which no large experimental tests have yet been made; (ii) while it is believed that the rank based method, named Borda Count, is competitive with score based methods, we will show that this is not true for metasearch; and (iii) we will show that Markov chain based methods compete with score based methods. This is especially important in the context of metasearch as scores are usually not available from the search engines.

235 citations

Journal ArticleDOI
TL;DR: A diverse relevance ranking scheme that is able to take relevance and diversity into account by exploring the content of images and their associated tags, and it is shown that the diversity of search results can be enhanced while maintaining a comparable level of relevance.
Abstract: Recent years have witnessed the great success of social media websites. Tag-based image search is an important approach to accessing the image content on these websites. However, the existing ranking methods for tag-based image search frequently return results that are irrelevant or not diverse. This paper proposes a diverse relevance ranking scheme that is able to take relevance and diversity into account by exploring the content of images and their associated tags. First, it estimates the relevance scores of images with respect to the query term based on both the visual information of images and the semantic information of associated tags. Then, we estimate the semantic similarities of social images based on their tags. Based on the relevance scores and the similarities, the ranking list is generated by a greedy ordering algorithm which optimizes average diverse precision, a novel measure that is extended from the conventional average precision. Comprehensive experiments and user studies demonstrate the effectiveness of the approach. We also apply the scheme for web image search reranking, and it is shown that the diversity of search results can be enhanced while maintaining a comparable level of relevance.

234 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