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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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Journal ArticleDOI
TL;DR: A support‐vector‐machine‐based single‐model global quality assessment (SVMQA) method that outperformed the existing best single‐ model QA methods both in ranking provided protein models and in selecting the best model from the pool.
Abstract: Motivation The accurate ranking of predicted structural models and selecting the best model from a given candidate pool remain as open problems in the field of structural bioinformatics. The quality assessment (QA) methods used to address these problems can be grouped into two categories: consensus methods and single-model methods. Consensus methods in general perform better and attain higher correlation between predicted and true quality measures. However, these methods frequently fail to generate proper quality scores for native-like structures which are distinct from the rest of the pool. Conversely, single-model methods do not suffer from this drawback and are better suited for real-life applications where many models from various sources may not be readily available. Results In this study, we developed a support-vector-machine-based single-model global quality assessment (SVMQA) method. For a given protein model, the SVMQA method predicts TM-score and GDT_TS score based on a feature vector containing statistical potential energy terms and consistency-based terms between the actual structural features (extracted from the three-dimensional coordinates) and predicted values (from primary sequence). We trained SVMQA using CASP8, CASP9 and CASP10 targets and determined the machine parameters by 10-fold cross-validation. We evaluated the performance of our SVMQA method on various benchmarking datasets. Results show that SVMQA outperformed the existing best single-model QA methods both in ranking provided protein models and in selecting the best model from the pool. According to the CASP12 assessment, SVMQA was the best method in selecting good-quality models from decoys in terms of GDTloss. Availability and implementation SVMQA method can be freely downloaded from http://lee.kias.re.kr/SVMQA/SVMQA_eval.tar.gz. Contact jlee@kias.re.kr. Supplementary information Supplementary data are available at Bioinformatics online.

135 citations

Journal ArticleDOI
TL;DR: The best learned functions, when evaluated against the best baseline function (BM25), demonstrate some significant performance differences, with improvements in mean average precision as high as 32% observed on one TREC collection not used in training.
Abstract: New general purpose ranking functions are discovered using genetic programming. The TREC WSJ collection was chosen as a training set. A baseline comparison function was chosen as the best of inner product, probability, cosine, and Okapi BM25. An elitist genetic algorithm with a population size 100 was run 13 times for 100 generations and the best performing algorithms chosen from these. The best learned functions, when evaluated against the best baseline function (BM25), demonstrate some significant performance differences, with improvements in mean average precision as high as 32% observed on one TREC collection not used in training. In no test is BM25 shown to significantly outperform the best learned function.

134 citations

Proceedings ArticleDOI
09 Dec 2007
TL;DR: This tutorial focuses on indifference-zone R&S procedures that provide a guaranteed probability of correct selection when the best system is at least a user-specified amount better than the other systems.
Abstract: This tutorial provides an overview on recent advances made in ranking and selection (R&S) for selecting the best simulated system and discusses challenges that still exist in the field. We focus on indifference-zone R&S procedures that provide a guaranteed probability of correct selection when the best system is at least a user-specified amount better than the other systems.

134 citations

Proceedings ArticleDOI
03 Jun 2013
TL;DR: CASSARAM is presented, a context-aware sensor search, selection, and ranking model for Internet of Things to address the research challenges of selecting sensors when large numbers of sensors with overlapping and sometimes redundant functionality are available.
Abstract: As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a substantial acceleration of the growth rate in the future. It is also evident that the increasing number of IoT middleware solutions are developed in both research and commercial environments. However, sensor search and selection remain a critical requirement and a challenge. In this paper, we present CASSARAM, a context-aware sensor search, selection, and ranking model for Internet of Things to address the research challenges of selecting sensors when large numbers of sensors with overlapping and sometimes redundant functionality are available. CASSARAM proposes the search and selection of sensors based on user priorities. CASSARAM considers a broad range of characteristics of sensors for search such as reliability, accuracy, battery life just to name a few. Our approach utilises both semantic querying and quantitative reasoning techniques. User priority based weighted Euclidean distance comparison in multidimensional space technique is used to index and rank sensors. Our objectives are to highlight the importance of sensor search in IoT paradigm, identify important characteristics of both sensors and data acquisition processes which help to select sensors, understand how semantic and statistical reasoning can be combined together to address this problem in an efficient manner. We developed a tool called CASSARA to evaluate the proposed model in terms of resource consumption and response time.

134 citations

Journal ArticleDOI
TL;DR: A new query suggestion scheme named Visual Query Suggestion (VQS) is proposed which is dedicated to image search and provides a more effective query interface to help users to precisely express their search intents by joint text and image suggestions.
Abstract: Query suggestion is an effective approach to bridge the Intention Gap between the users' search intents and queries 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 article proposes a new query suggestion scheme named Visual Query Suggestion (VQS) which is dedicated to image search VQS provides a more effective query interface to help users to precisely express their search intents by joint text and image suggestions When a user submits a textual query, VQS first provides a list of suggestions, each containing a keyword and a collection of representative images in a dropdown menu Once the user selects one of the suggestions, the corresponding keyword will be added to complement the initial query as the new textual query, while the image collection will be used as the visual query to further represent the search intent VQS then performs image search based on the new textual 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 against three popular image search engines, and show that VQS outperforms these engines in terms of both the quality of query suggestion and the search performance

134 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