scispace - formally typeset
Search or ask a question
Topic

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
More filters
Book ChapterDOI
23 Sep 2009
TL;DR: “background samples”, that is, examples which do not belong to any of the classes being learned, may provide a significant performance boost to such face recognition systems, and is defined and evaluated as an extension to the recently proposed “One-Shot Similarity” (OSS) measure.
Abstract: Evaluating the similarity of images and their descriptors by employing discriminative learners has proven itself to be an effective face recognition paradigm. In this paper we show how “background samples”, that is, examples which do not belong to any of the classes being learned, may provide a significant performance boost to such face recognition systems. In particular, we make the following contributions. First, we define and evaluate the “Two-Shot Similarity” (TSS) score as an extension to the recently proposed “One-Shot Similarity” (OSS) measure. Both these measures utilize background samples to facilitate better recognition rates. Second, we examine the ranking of images most similar to a query image and employ these as a descriptor for that image. Finally, we provide results underscoring the importance of proper face alignment in automatic face recognition systems. These contributions in concert allow us to obtain a success rate of 86.83% on the Labeled Faces in the Wild (LFW) benchmark, outperforming current state-of-the-art results.

314 citations

Proceedings ArticleDOI
21 Apr 2008
TL;DR: A general ranking framework for factual information retrieval from social media is presented and results of a large scale evaluation demonstrate that the method is highly effective at retrieving well-formed, factual answers to questions, as evaluated on a standard factoid QA benchmark.
Abstract: Community Question Answering has emerged as a popular and effective paradigm for a wide range of information needs. For example, to find out an obscure piece of trivia, it is now possible and even very effective to post a question on a popular community QA site such as Yahoo! Answers, and to rely on other users to provide answers, often within minutes. The importance of such community QA sites is magnified as they create archives of millions of questions and hundreds of millions of answers, many of which are invaluable for the information needs of other searchers. However, to make this immense body of knowledge accessible, effective answer retrieval is required. In particular, as any user can contribute an answer to a question, the majority of the content reflects personal, often unsubstantiated opinions. A ranking that combines both relevance and quality is required to make such archives usable for factual information retrieval. This task is challenging, as the structure and the contents of community QA archives differ significantly from the web setting. To address this problem we present a general ranking framework for factual information retrieval from social media. Results of a large scale evaluation demonstrate that our method is highly effective at retrieving well-formed, factual answers to questions, as evaluated on a standard factoid QA benchmark. We also show that our learning framework can be tuned with the minimum of manual labeling. Finally, we provide result analysis to gain deeper understanding of which features are significant for social media search and retrieval. Our system can be used as a crucial building block for combining results from a variety of social media content with general web search results, and to better integrate social media content for effective information access.

313 citations

Proceedings ArticleDOI
07 Dec 2015
TL;DR: Zhang et al. as mentioned in this paper proposed a dual attribute-aware ranking network (DARN) for cross-domain image retrieval, which consists of two sub-networks, one for each domain, whose retrieval feature representations are driven by semantic attribute learning.
Abstract: We address the problem of cross-domain image retrieval, considering the following practical application: given a user photo depicting a clothing image, our goal is to retrieve the same or attribute-similar clothing items from online shopping stores. This is a challenging problem due to the large discrepancy between online shopping images, usually taken in ideal lighting/pose/background conditions, and user photos captured in uncontrolled conditions. To address this problem, we propose a Dual Attribute-aware Ranking Network (DARN) for retrieval feature learning. More specifically, DARN consists of two sub-networks, one for each domain, whose retrieval feature representations are driven by semantic attribute learning. We show that this attribute-guided learning is a key factor for retrieval accuracy improvement. In addition, to further align with the nature of the retrieval problem, we impose a triplet visual similarity constraint for learning to rank across the two subnetworks. Another contribution of our work is a large-scale dataset which makes the network learning feasible. We exploit customer review websites to crawl a large set of online shopping images and corresponding offline user photos with fine-grained clothing attributes, i.e., around 450,000 online shopping images and about 90,000 exact offline counterpart images of those online ones. All these images are collected from real-world consumer websites reflecting the diversity of the data modality, which makes this dataset unique and rare in the academic community. We extensively evaluate the retrieval performance of networks in different configurations. The top-20 retrieval accuracy is doubled when using the proposed DARN other than the current popular solution using pre-trained CNN features only (0.570 vs. 0.268).

312 citations

Patent
23 Sep 2011
TL;DR: In one embodiment, the method comprises receiving an input query; conducting a search to identify candidate answers to the input query, and producing a plurality of scores for each of the candidate answers as discussed by the authors.
Abstract: A method, system and computer program product for generating answers to questions. In one embodiment, the method comprises receiving an input query; conducting a search to identify candidate answers to the input query, and producing a plurality of scores for each of the candidate answers. For each of the candidate answers, one, of a plurality of candidate ranking functions, is selected. This selected ranking function is applied to the each of the candidate answers to determine a ranking for the candidate answer based on the scores for that candidate answer. One or more of the candidate answers is selected, based on the rankings for the candidate answers, as one or more answers to the input query. In an embodiment, the ranking function selection is performed using information about the question. In an embodiment, the ranking function selection is performed using information about each answer.

312 citations

Journal ArticleDOI
TL;DR: A new method based on the ranking of generalized trapezoidal fuzzy numbers for fuzzy risk analysis that can overcome the drawbacks of the existing centroid-index ranking methods is presented.
Abstract: In this paper, we present a new method for fuzzy risk analysis based on the ranking of generalized trapezoidal fuzzy numbers. The proposed method considers the centroid points and the standard deviations of generalized trapezoidal fuzzy numbers for ranking generalized trapezoidal fuzzy numbers. We also use an example to compare the ranking results of the proposed method with the existing centroid-index ranking methods. The proposed ranking method can overcome the drawbacks of the existing centroid-index ranking methods. Based on the proposed ranking method, we also present an algorithm to deal with fuzzy risk analysis problems. The proposed fuzzy risk analysis algorithm can overcome the drawbacks of the one we presented in [7].

311 citations


Network Information
Related Topics (5)
Web page
50.3K papers, 975.1K citations
83% related
Ontology (information science)
57K papers, 869.1K citations
82% related
Graph (abstract data type)
69.9K papers, 1.2M citations
82% related
Feature learning
15.5K papers, 684.7K citations
81% related
Supervised learning
20.8K papers, 710.5K citations
81% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168