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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
19 Oct 2009
TL;DR: This paper proposes a new query suggestion scheme named Visual Query Suggestion (VQS), which provides a more effective query interface to formulate an intent-specific query by joint text and image suggestions, and shows that VQS outperforms these engines in terms of both the quality of query suggestion and search performance.
Abstract: Query suggestion is an effective approach to improve the usability of image search. Most existing search engines are able to automatically suggest a list of textual query terms based on users' current query input, which can be called Textual Query Suggestion. This paper proposes a new query suggestion scheme named Visual Query Suggestion (VQS) which is dedicated to image search. It provides a more effective query interface to formulate an intent-specific query by joint text and image suggestions. We show that VQS is able to more precisely and more quickly help users specify and deliver their search intents. When a user submits a text query, VQS first provides a list of suggestions, each containing a keyword and a collection of representative images in a dropdown menu. If the user selects one of the suggestions, the corresponding keyword will be added to complement the initial text query as the new text query, while the image collection will be formulated as the visual query. VQS then performs image search based on the new text query using text search techniques, as well as content-based visual retrieval to refine the search results by using the corresponding images as query examples. We compare VQS with three popular image search engines, and show that VQS outperforms these engines in terms of both the quality of query suggestion and search performance.

188 citations

Journal ArticleDOI
Zeshui Xu1, Na Zhao1
TL;DR: An overview on the existing intuitionism fuzzy decision making theories and methods from the perspective of information fusion, involving the determination of attribute weights, the aggregation of intuitionistic fuzzy information and the ranking of alternatives is presented.

188 citations

Proceedings ArticleDOI
01 Apr 1997
TL;DR: It is argued that delegating the task of meta-data collection to local index servers is a more scalable approach, and a mechanism for integrating distributed autonomous index servers into a cooperative resource discovery system is proposed.
Abstract: Keyword-based search services have become necessary tools for nding information resources on the Internet today. The rapid growth of information on the Internet renders centralized keyword index services incapable of collecting comprehensive resource meta-data in a timely manner. We argue that delegating the task of meta-data collection to local index servers is a more scalable approach. We propose a mechanism for integrating distributed autonomous index servers into a cooperative resource discovery system. Focusing on the retrieval eeec-tiveness of the system, we propose a set of methods , called CVV-based methods, for ranking and selecting index servers with respect to a query, and merging the results returned by the index servers. Through experiments, we evaluate the eeectiveness of the CVV-based methods, and compare our server ranking method with methods proposed by other researchers .

187 citations

Proceedings ArticleDOI
19 Oct 2009
TL;DR: A novel ranking algorithm, namely ranking with Local Regression and Global Alignment (LRGA), which learns a robust Laplacian matrix for data ranking and a unified objective function to globally align the local models from all the data points so that an optimal ranking value can be assigned to each data point.
Abstract: Rich multimedia content including images, audio and text are frequently used to describe the same semantics in E-Learning and Ebusiness web pages, instructive slides, multimedia cyclopedias, and so on. In this paper, we present a framework for cross-media retrieval, where the query example and the retrieved result(s) can be of different media types. We first construct Multimedia Correlation Space (MMCS) by exploring the semantic correlation of different multimedia modalities, during which multimedia content and co-occurrence information is utilized. We propose a novel ranking algorithm, namely ranking with Local Regression and Global Alignment (LRGA), which learns a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking values of its neighboring points. We propose a unified objective function to globally align the local models from all the data points so that an optimal ranking value can be assigned to each data point. LRGA is insensitive to parameters, making it particularly suitable for data ranking. A relevance feedback algorithm is proposed to improve the retrieval performance. Comprehensive experiments have demonstrated the effectiveness of our methods.

187 citations

Journal ArticleDOI
Yiyu Yao1
TL;DR: A new measure of system performance is suggested based on the distance between user ranking and system ranking that only uses the relative order of documents and therefore confirms to the valid use of an ordinal scale measuring relevance.
Abstract: The notion of user preference is adopted for the representation, interpretation, and measurement of the relevance or usefulness of documents. User judgments on documents may be formally described by a weak order (i.e., user ranking) and measured using an ordinal scale. Within this framework, a new measure of system performance is suggested based on the distance between user ranking and system ranking. It only uses the relative order of documents and therefore confirms to the valid use of an ordinal scale measuring relevance. It is also applicable to multilevel relevance judgments and ranked system output. The appropriateness of the proposed measure is demonstrated through an axiomatic approach. The inherent relationships between the new measure and many existing measures provide further supporting evidence

187 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