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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
07 Sep 1999
TL;DR: A framework for multidatabase query processing that fully includes the quality of information in many facets, such as completeness, timeliness, accuracy, etc, is described.
Abstract: Integrated access to information that is spread over multiple, distributed, and heterogeneous sources is an important problem in many scienti c and commercial domains. While much work has been done on query processing and choosing plans under cost criteria, very little is known about the important problem of incorporating the information quality aspect into query planning. In this paper we describe a framework for multidatabase query processing that fully includes the quality of information in many facets, such as completeness, timeliness, accuracy, etc. We seamlessly include information quality into a multidatabase query processor based on a view-rewriting mechanism. We model information quality at di erent levels to ultimately nd a set of high-quality queryanswering plans.

243 citations

Journal ArticleDOI
TL;DR: This paper presents a verifiable privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking and proposes two secure index schemes to meet the stringent privacy requirements under strong threat models.
Abstract: With the growing popularity of cloud computing, huge amount of documents are outsourced to the cloud for reduced management cost and ease of access. Although encryption helps protecting user data confidentiality, it leaves the well-functioning yet practically-efficient secure search functions over encrypted data a challenging problem. In this paper, we present a verifiable privacy-preserving multi-keyword text search (MTS) scheme with similarity-based ranking to address this problem. To support multi-keyword search and search result ranking, we propose to build the search index based on term frequency and the vector space model with cosine similarity measure to achieve higher search result accuracy. To improve the search efficiency, we propose a tree-based index structure and various adaptive methods for multi-dimensional (MD) algorithm so that the practical search efficiency is much better than that of linear search. To further enhance the search privacy, we propose two secure index schemes to meet the stringent privacy requirements under strong threat models, i.e., known ciphertext model and known background model. In addition, we devise a scheme upon the proposed index tree structure to enable authenticity check over the returned search results. Finally, we demonstrate the effectiveness and efficiency of the proposed schemes through extensive experimental evaluation.

243 citations

Patent
12 Jun 2003
TL;DR: In this article, a user interface can present results in the form of browsing multiple hierarchical representations, wherein matching categories are differentiated from non-matching categories, providing an indication of the fitness of the search terms for returning satisfactory results.
Abstract: Systems and methods for data storage, retrieval, manipulation and display provide search engines and computer-based research tools for enabling multiple hierarchical points of view. Category definitions in the hierarchical data structures can include lists of set members, like word arrays of set members, generative descriptions for determining set members, and fitness functions for determining fitness of a presented item for being a member of a set. Significance and interest values can be assigned to search categories to set threshold confidence levels for returning search results and for weighting the results, respectively. A user interface can present results in the form of browsing multiple hierarchical representations, wherein matching categories are differentiated from non-matching categories. Peer ratings can represent the ranking of search term results with relation to results using other search terms, providing an indication of the fitness of the search terms for returning satisfactory results.

243 citations

Patent
14 Feb 2008
TL;DR: In this article, the authors proposed a method to retrieve the pages according to the quality of the individual pages, where the rank of a page for a keyword is a combination of intrinsic and extrinsic ranks.
Abstract: The present invention provides systems and methods of retrieving the pages according to the quality of the individual pages. The rank of a page for a keyword is a combination of intrinsic and extrinsic ranks. Intrinsic rank is the measure of the relevancy of a page to a given keyword as claimed by the author of the page while extrinsic rank is a measure of the relevancy of a page on a given keyword as indicated by other pages. The former is obtained from the analysis of the keyword matching in various parts of the page while the latter is obtained from the context-sensitive connectivity analysis of the links connecting the entire Web. The present invention also provides the methods to solve the self-consistent equation satisfied by the page weights iteratively in a very efficient way. The ranking mechanism for multi-word query is also described. Finally, the present invention provides a method to obtain the more relevant page weights by dividing the entire hypertext pages into distinct number of groups.

241 citations

Proceedings ArticleDOI
15 Jun 2019
TL;DR: A Class Activation Maps (CAM) augmentation model is proposed to expand the activation scope of baseline Re-ID model to explore rich visual cues, where the backbone network is extended by a series of ordered branches which share the same input but output complementary CAM.
Abstract: The fundamental challenge of small inter-person variation requires Person Re-Identification (Re-ID) models to capture sufficient fine-grained information. This paper proposes to discover diverse discriminative visual cues without extra assistance, e.g., pose estimation, human parsing. Specifically, a Class Activation Maps (CAM) augmentation model is proposed to expand the activation scope of baseline Re-ID model to explore rich visual cues, where the backbone network is extended by a series of ordered branches which share the same input but output complementary CAM. A novel Overlapped Activation Penalty is proposed to force the new branch to pay more attention to the image regions less activated by the old ones, such that spatial diverse visual features can be discovered. The proposed model achieves state-of-the-art results on three person Re-ID benchmarks. Moreover, a visualization approach termed ranking activation map (RAM) is proposed to explicitly interpret the ranking results in the test stage, which gives qualitative validations of the proposed method.

240 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