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Academic literature on the topic 'Ranking algorithms'

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Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Ranking algorithms"

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Hull, Roger. "Ranking algorithms." New Scientist 215, no. 2881 (September 2012): 28. http://dx.doi.org/10.1016/s0262-4079(12)62328-8.

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Rieder, Bernhard, Ariadna Matamoros-Fernández, and Òscar Coromina. "From ranking algorithms to ‘ranking cultures’." Convergence: The International Journal of Research into New Media Technologies 24, no. 1 (January 10, 2018): 50–68. http://dx.doi.org/10.1177/1354856517736982.

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Algorithms, as constitutive elements of online platforms, are increasingly shaping everyday sociability. Developing suitable empirical approaches to render them accountable and to study their social power has become a prominent scholarly concern. This article proposes an approach to examine what an algorithm does, not only to move closer to understanding how it works, but also to investigate broader forms of agency involved. To do this, we examine YouTube’s search results ranking over time in the context of seven sociocultural issues. Through a combination of rank visualizations, computational change metrics and qualitative analysis, we study search ranking as the distributed accomplishment of ‘ranking cultures’. First, we identify three forms of ordering over time – stable, ‘newsy’ and mixed rank morphologies. Second, we observe that rankings cannot be easily linked back to popularity metrics, which highlights the role of platform features such as channel subscriptions in processes of visibility distribution. Third, we find that the contents appearing in the top 20 results are heavily influenced by both issue and platform vernaculars. YouTube-native content, which often thrives on controversy and dissent, systematically beats out mainstream actors in terms of exposure. We close by arguing that ranking cultures are embedded in the meshes of mutually constitutive agencies that frustrate our attempts at causal explanation and are better served by strategies of ‘descriptive assemblage’.
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Magri, Giorgio. "Convergence of error-driven ranking algorithms." Phonology 29, no. 2 (August 2012): 213–69. http://dx.doi.org/10.1017/s0952675712000127.

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AbstractAccording to the OT error-driven ranking model of language acquisition, the learner performs a sequence of slight re-rankings triggered by mistakes on the incoming stream of data, until it converges to a ranking that makes no more mistakes. Two classical examples are Tesar & Smolensky's (1998) Error-Driven Constraint Demotion (EDCD) and Boersma's (1998) Gradual Learning Algorithm (GLA). Yet EDCD only performs constraint demotion, and is thus shown to predict a ranking dynamics which is too simple from a modelling perspective. The GLA performs constraint promotion too, but has been shown not to converge. This paper develops a complete theory of convergence of error-driven ranking algorithms that perform both constraint demotion and promotion. In particular, it shows that convergent constraint promotion can be achieved (with an error-bound that compares well to that of EDCD) through a proper calibration of the amount by which constraints are promoted.
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Wang, Chao, Jie Ding, and Bin Hu. "Ranking Algorithms for Keyword Search over Relational Databases." Advanced Materials Research 605-607 (December 2012): 2291–96. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2291.

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Developing effective ranking algorithms for keyword search over relational databases is a hot study topic. Ranking algorithm largely determines the performance of a keyword search system. Good ranking algorithms not only provide user with the most relevant query results but also provide fast response time. A number of existing ranking algorithms were classified and compared. Several representational algorithms were summarized and analysed in detail. The principles, advantages and disadvantages of these algorithms were discussed. Finally, prospect for future work, especially the intelligent trends, in ranking were discussed.
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XUAN, QI, CHENBO FU, and LI YU. "RANKING DEVELOPER CANDIDATES BY SOCIAL LINKS." Advances in Complex Systems 17, no. 07n08 (December 2014): 1550005. http://dx.doi.org/10.1142/s0219525915500058.

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In open source software (OSS) projects, participants initially communicate with others and then may become developers if they are deemed worthy by the community. Recent studies indicate that the abundance of established social links of a participant is the strongest predictor to his/her promotion. Having reliable rankings of the candidates is key to recruiting and maintaining a successful operation of an OSS project. This paper adopts degree-based, PageRank, and Hits ranking algorithms to rank developer candidates in OSS projects based on their social links. We construct several types of social networks based on the communications between the participants in Apache OSS projects, then train and test the ranking algorithms in these networks. We find that, for all the ranking algorithms under study, the rankings of emergent developers in temporal networks are higher than those in cumulative ones, indicating that the more recent communications of a developer in a project are more important to predict his/her first commit in the project. By comparison, the simple degree-based and the PageRank ranking algorithms in temporal undirected weighted networks behave better than the others in identifying emergent developers based on four performance indicators, and are thus recommended to be applied in the future.
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Rahayu, Syarifah Bahiyah. "Ranking Algorithm for Semantic Document Annotations." International Journal of Information Retrieval Research 2, no. 1 (January 2012): 1–10. http://dx.doi.org/10.4018/ijirr.2012010101.

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Semantic annotation represents a metadata of the document based on domain ontology. The purpose of this paper is to develop a ranking algorithm for semantic document annotation and to evaluate its performance in the Semantic Web (SW) application. The evaluation is to compare the ranking algorithm against other algorithms. For the evaluation purpose, all the algorithms are applied into the SW application. The SW application is a research prototype retrieval engine, PicoDoc. The system framework of PicoDoc is based on OCAS2008 ontology. During the experimentation stage, a real-life dataset from news article corpus of ABC and BBC websites are selected. The experiment shows promising results in retrieving related information using the ranking algorithm.
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Lin, Hsuan-Tien, and Ling Li. "Reduction from Cost-Sensitive Ordinal Ranking to Weighted Binary Classification." Neural Computation 24, no. 5 (May 2012): 1329–67. http://dx.doi.org/10.1162/neco_a_00265.

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We present a reduction framework from ordinal ranking to binary classification. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranker from the binary classifier. Based on the framework, we show that a weighted 0/1 loss of the binary classifier upper-bounds the mislabeling cost of the ranker, both error-wise and regret-wise. Our framework allows not only the design of good ordinal ranking algorithms based on well-tuned binary classification approaches, but also the derivation of new generalization bounds for ordinal ranking from known bounds for binary classification. In addition, our framework unifies many existing ordinal ranking algorithms, such as perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms. In addition, the newly designed algorithms lead to better cost-sensitive ordinal ranking performance, as well as improved listwise ranking performance.
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Duchi, John C., Lester Mackey, and Michael I. Jordan. "The asymptotics of ranking algorithms." Annals of Statistics 41, no. 5 (October 2013): 2292–323. http://dx.doi.org/10.1214/13-aos1142.

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Chang, Chia-Jung, and Kun-Mao Chao. "Efficient algorithms for local ranking." Information Processing Letters 112, no. 13 (July 2012): 517–22. http://dx.doi.org/10.1016/j.ipl.2012.03.011.

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PENG, ZEWU, YAN PAN, YONG TANG, and GUOHUA CHEN. "A RELATIONAL RANKING METHOD WITH GENERALIZATION ANALYSIS." International Journal on Artificial Intelligence Tools 21, no. 03 (June 2012): 1250021. http://dx.doi.org/10.1142/s0218213012500212.

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Recently, learning to rank, which aims at constructing a model for ranking objects, is one of the hot research topics in information retrieval and machine learning communities. Most of existing learning to rank approaches are based on the assumption that each object is independently and identically distributed. Although this assumption simplifies ranking problems, the implicit interconnections between objects are ignored. In this paper, a graph based ranking framework is proposed, which takes advantage of implicit correlations between objects. Furthermore, the derived relational ranking algorithm from this framework, called GRSVM, is developed based on the conventional algorithm RankSVM-primal. In addition, generalization properties of different relational ranking algorithms are analyzed using Rademacher Average. Based on the analysis, we find that GRSVM can achieve tighter generalization bound than existing relational ranking algorithms in most cases. Finally, a comparison of experimental results produced by improved and conventional algorithms shows the superior performance of the former.
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Dissertations / Theses on the topic "Ranking algorithms"

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Xu, Liqun. "Algorithms for random ranking generation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0021/MQ54338.pdf.

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Wong, Brian Wai Fung. "Deep-web search engine ranking algorithms." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61246.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 79-80).
The deep web refers to content that is hidden behind HTML forms. The deep web contains a large collection of data that are unreachable by link-based search engines. A study conducted at University of California, Berkeley estimated that the deep web consists of around 91,000 terabytes of data, whereas the surface web is only about 167 terabytes. To access this content, one must submit valid input values to the HTML form. Several researchers have studied methods for crawling deep web content. One of the most promising methods uses unique wrappers for HTML forms. User inputs are first filtered through the wrappers before being submitted to the forms. However, this method requires a new algorithm for ranking search results generated by the wrappers. In this paper, I explore methods for ranking search results returned from a wrapped-based deep web search engine.
by Brian Wai Fung Wong.
M.Eng.
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Trailović, Lidija. "Ranking and optimization of target tracking algorithms." online access from Digital Dissertation Consortium access full-text, 2002. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?3074810.

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Spanias, Demetris. "Professional tennis : quantitative models and ranking algorithms." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24813.

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Professional singles tennis is a popular global sport that attracts spectators and speculators alike. In recent years, financial trading related to sport outcomes has become a reality, thanks to the rise of online betting exchanges and the ever increasing development and deployment of quantitative models for sports. This thesis investigates the extent to which the outcome of a match between two professional tennis players can be forecast using quantitative models parameterised by historical data. Three different approaches are explored, each having its own advantages and disadvantages. Firstly, the problem is approached using a Markov chain to model a tennis point, estimating the probability of a player winning a point while serving. Such a probability can be used as a parameter to existing hierarchical models to estimate the probability of a player winning the match. We demonstrate how this probability can be estimated using varying subsets of historical player data and investigate their effect on results. Averaged historical data over varying opponents with different skill sets, does not necessarily provide a fair basis of comparison when evaluating the performance of players. The second approach presented is a technique that uses data, which includes only matches played against common opponents, to find the difference between the modelled players' probability of winning a point on their serve against each common opponent. This difference in probability for each common opponent is a 'transitive contribution' towards victory for the match being modelled. By combining these 'contributions' the 'Common-Opponent' model overcomes the problems of using average historical statistics at the cost of a shrinking data set. Finally, the thesis ventures into the field of player rankings. Rankings provide a fast and simple method for predicting match winners and comparing players. We present a variety of methods to generate such player rankings, either by making use of network analysis or hierarchical models. The generated rankings are then evaluated using their ability to correctly represent the subset of matches that were used to generate them as well as their ability to forecast future matches.
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Trotman, Andrew, and n/a. "Searching and ranking structured documents." University of Otago. Department of Computer Science, 2007. http://adt.otago.ac.nz./public/adt-NZDU20070403.110440.

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It is common to see documents with explicit structure marked up in languages such as XML. Queries, on the other hand, typically have no structure. There is a clear mismatch, although documents contain structure it is typically not used in information retrieval. An efficient index structure for document-centric searching is proposed and its efficiency is discussed. It is shown to be at worst linear with respect to the number of occurrences of a given search term. The algorithm is then extended to accommodate element-centric information retrieval. Ranking algorithms for structured documents are examined. Genetic Algorithms are used to learn different weights for each structure present in a document. Applying these weights as part of a function is shown to yield significant precision improvements in some functions. Genetic Programming is then used to learn an entire ranking function. This function is shown to be portable between document collections. A query language for structured information retrieval is proposed. Use of this language in the 2004 INEX workshop resulted in a large decrease in query errors. Structured information retrieval is now a viable alternative to its unstructured counterpart. A successful query language, efficient indexing structures, and improved ranking functions are all presented.
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Dunaiski, Marcel Paul. "Analysing ranking algorithms and publication trends on scholarly citation networks." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/96106.

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Thesis (MSc)--Stellenbosch University, 2014.
ENGLISH ABSTRACT: Citation analysis is an important tool in the academic community. It can aid universities, funding bodies, and individual researchers to evaluate scientific work and direct resources appropriately. With the rapid growth of the scientific enterprise and the increase of online libraries that include citation analysis tools, the need for a systematic evaluation of these tools becomes more important. The research presented in this study deals with scientific research output, i.e., articles and citations, and how they can be used in bibliometrics to measure academic success. More specifically, this research analyses algorithms that rank academic entities such as articles, authors and journals to address the question of how well these algorithms can identify important and high-impact entities. A consistent mathematical formulation is developed on the basis of a categorisation of bibliometric measures such as the h-index, the Impact Factor for journals, and ranking algorithms based on Google’s PageRank. Furthermore, the theoretical properties of each algorithm are laid out. The ranking algorithms and bibliometric methods are computed on the Microsoft Academic Search citation database which contains 40 million papers and over 260 million citations that span across multiple academic disciplines. We evaluate the ranking algorithms by using a large test data set of papers and authors that won renowned prizes at numerous Computer Science conferences. The results show that using citation counts is, in general, the best ranking metric. However, for certain tasks, such as ranking important papers or identifying high-impact authors, algorithms based on PageRank perform better. As a secondary outcome of this research, publication trends across academic disciplines are analysed to show changes in publication behaviour over time and differences in publication patterns between disciplines.
AFRIKAANSE OPSOMMING: Sitasiesanalise is ’n belangrike instrument in die akademiese omgewing. Dit kan universiteite, befondsingsliggams en individuele navorsers help om wetenskaplike werk te evalueer en hulpbronne toepaslik toe te ken. Met die vinnige groei van wetenskaplike uitsette en die toename in aanlynbiblioteke wat sitasieanalise insluit, word die behoefte aan ’n sistematiese evaluering van hierdie gereedskap al hoe belangriker. Die navorsing in hierdie studie handel oor die uitsette van wetenskaplike navorsing, dit wil sê, artikels en sitasies, en hoe hulle gebruik kan word in bibliometriese studies om akademiese sukses te meet. Om meer spesifiek te wees, hierdie navorsing analiseer algoritmes wat akademiese entiteite soos artikels, outeers en journale gradeer. Dit wys hoe doeltreffend hierdie algoritmes belangrike en hoë-impak entiteite kan identifiseer. ’n Breedvoerige wiskundige formulering word ontwikkel uit ’n versameling van bibliometriese metodes soos byvoorbeeld die h-indeks, die Impak Faktor vir journaale en die rang-algoritmes gebaseer op Google se PageRank. Verder word die teoretiese eienskappe van elke algoritme uitgelê. Die rang-algoritmes en bibliometriese metodes gebruik die sitasiedatabasis van Microsoft Academic Search vir berekeninge. Dit bevat 40 miljoen artikels en meer as 260 miljoen sitasies, wat oor verskeie akademiese dissiplines strek. Ons gebruik ’n groot stel toetsdata van dokumente en outeers wat bekende pryse op talle rekenaarwetenskaplike konferensies gewen het om die rang-algoritmes te evalueer. Die resultate toon dat die gebruik van sitasietellings, in die algemeen, die beste rangmetode is. Vir sekere take, soos die gradeering van belangrike artikels, of die identifisering van hoë-impak outeers, presteer algoritmes wat op PageRank gebaseer is egter beter. ’n Sekondêre resultaat van hierdie navorsing is die ontleding van publikasie tendense in verskeie akademiese dissiplines om sodoende veranderinge in publikasie gedrag oor tyd aan te toon en ook die verskille in publikasie patrone uit verskillende dissiplines uit te wys.
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Sun, Mingxuan. "Visualizing and modeling partial incomplete ranking data." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45793.

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Analyzing ranking data is an essential component in a wide range of important applications including web-search and recommendation systems. Rankings are difficult to visualize or model due to the computational difficulties associated with the large number of items. On the other hand, partial or incomplete rankings induce more difficulties since approaches that adapt well to typical types of rankings cannot apply generally to all types. While analyzing ranking data has a long history in statistics, construction of an efficient framework to analyze incomplete ranking data (with or without ties) is currently an open problem. This thesis addresses the problem of scalability for visualizing and modeling partial incomplete rankings. In particular, we propose a distance measure for top-k rankings with the following three properties: (1) metric, (2) emphasis on top ranks, and (3) computational efficiency. Given the distance measure, the data can be projected into a low dimensional continuous vector space via multi-dimensional scaling (MDS) for easy visualization. We further propose a non-parametric model for estimating distributions of partial incomplete rankings. For the non-parametric estimator, we use a triangular kernel that is a direct analogue of the Euclidean triangular kernel. The computational difficulties for large n are simplified using combinatorial properties and generating functions associated with symmetric groups. We show that our estimator is computational efficient for rankings of arbitrary incompleteness and tie structure. Moreover, we propose an efficient learning algorithm to construct a preference elicitation system from partial incomplete rankings, which can be used to solve the cold-start problems in ranking recommendations. The proposed approaches are examined in experiments with real search engine and movie recommendation data.
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Zacharia, Giorgos 1974. "Regularized algorithms for ranking, and manifold learning for related tasks." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/47753.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Includes bibliographical references (leaves 119-127).
This thesis describes an investigation of regularized algorithms for ranking problems for user preferences and information retrieval problems. We utilize regularized manifold algorithms to appropriately incorporate data from related tasks. This investigation was inspired by personalization challenges in both user preference and information retrieval ranking problems. We formulate the ranking problem of related tasks as a special case of semi-supervised learning. We examine how to incorporate instances from related tasks, with the appropriate penalty in the loss function to optimize performance on the hold out sets. We present a regularized manifold approach that allows us to learn a distance metric for the different instances directly from the data. This approach allows incorporation of information from related task examples, without prior estimation of cross-task coefficient covariances. We also present applications of ranking problems in two text analysis problems: a) Supervise content-word learning, and b) Company Entity matching for record linkage problems.
by Giorgos Zacharia.
Ph.D.
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Halverson, Ranette Hudson. "Efficient Linked List Ranking Algorithms and Parentheses Matching as a New Strategy for Parallel Algorithm Design." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278153/.

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The goal of a parallel algorithm is to solve a single problem using multiple processors working together and to do so in an efficient manner. In this regard, there is a need to categorize strategies in order to solve broad classes of problems with similar structures and requirements. In this dissertation, two parallel algorithm design strategies are considered: linked list ranking and parentheses matching.
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Lee, Chun-fan, and 李俊帆. "Fitting factor models for ranking data using efficient EM-type algorithms." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31227557.

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Books on the topic "Ranking algorithms"

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Gündüz-Ögüdücü, Şule. Web page recommendation models: Theory and algorithms. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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Pattern Search Ranking and Selection Algorithms for Mixed-Variable Optimization of Stochastic Systems. Storming Media, 2004.

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Bleakley, Chris. Poems That Solve Puzzles. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198853732.001.0001.

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Algorithms are the hidden methods that computers apply to process information and make decisions. The book tells the story of algorithms from their ancient origins to the present day and beyond. The book introduces readers to the inventors and events behind the genesis of the world’s most important algorithms. Along the way, it explains, with the aid of examples and illustrations, how the most influential algorithms work. The first algorithms were invented in Mesopotamia 4,000 years ago. The ancient Greeks refined the concept, creating algorithms for finding prime numbers and enumerating Pi. Al-Khawrzmi’s 9th century books on algorithms ultimately became their conduit to the West. The invention of the electronic computer during World War II transformed the importance of the algorithm. The first computer algorithms were for military applications. In peacetime, researchers turned to grander challenges - forecasting the weather, route navigation, choosing marriage partners, and creating artificial intelligences. The success of the Internet in the 70s depended on algorithms for transporting data and correcting errors. A clever algorithm for ranking websites was the spark that ignited Google. Recommender algorithms boosted sales at Amazon and Netflix, while the EdgeRank algorithm drove Facebook’s NewsFeed. In the 21st century, an algorithm that mimics the operation of the human brain was revisited with the latest computer technology. Suddenly, algorithms attained human-level accuracy in object and speech recognition. An algloirthm defeated the world champion at Go - the most complex of board games. Today, algorithms for cryptocurrencies and quantum computing look set to change the world.
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Bucher, Taina. If...Then. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190493028.001.0001.

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IF … THEN provides an account of power and politics in the algorithmic media landscape that pays attention to the multiple realities of algorithms, and how these relate and coexist. The argument is made that algorithms do not merely have power and politics; they help to produce certain forms of acting and knowing in the world. In processing, classifying, sorting, and ranking data, algorithms are political in that they help to make the world appear in certain ways rather than others. Analyzing Facebook’s news feed, social media user’s everyday encounters with algorithmic systems, and the discourses and work practices of news professionals, the book makes a case for going beyond the narrow, technical definition of algorithms as step-by-step procedures for solving a problem in a finite number of steps. Drawing on a process-relational theoretical framework and empirical data from field observations and fifty-five interviews, the author demonstrates how algorithms exist in multiple ways beyond code. The analysis is concerned with the world-making capacities of algorithms, questioning how algorithmic systems shape encounters and orientations of different kinds, and how these systems are endowed with diffused personhood and relational agency. IF … THEN argues that algorithmic power and politics is neither about algorithms determining how the social world is fabricated nor about what algorithms do per se. Rather it is about how and when different aspects of algorithms and the algorithmic become available to specific actors, under what circumstance, and who or what gets to be part of how algorithms are defined.
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An evaluation of the applicability of ranking algorithms to improving the effectiveness of full text retrieval. Ann Arbor, Mich: University Microfilms International, 1986.

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Newman, Mark. Network search. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0018.

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This chapter gives a discussion of search processes on networks. It begins with a discussion of web search, including crawlers and web ranking algorithms such as PageRank. Search in distributed databases such as peer-to-peer networks is also discussed, including simple breadth-first search style algorithms and more advanced “supernode” approaches. Finally, network navigation is discussed at some length, motivated by consideration of Milgram's letter passing experiment. Kleinberg's variant of the small-world model is introduced and it is shown that efficient navigation is possible only for certain values of the model parameters. Similar results are also derived for the hierarchical model of Watts et al.
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Algorithms And Models For The Webgraph 6th International Workshop Waw 2009 Barcelona Spain February 1213 2009 Proceedings. Springer, 2009.

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Olivas Varela, José Ángel. Búsqueda eficaz de información en la web. Editorial de la Universidad Nacional de La Plata (EDULP), 2011. http://dx.doi.org/10.35537/10915/18401.

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En este trabajo se describe someramente lo que es un Sistema de Recuperación de Información, para posteriormente poder profundizar en algunos aspectos específicos. Se presentan las herramientas de búsqueda Web más usadas actualmente, haciendo especial hincapié en los buscadores y en los metabuscadores, con el fin de proporcionar ciertos “trucos” para ayudar a mejorar nuestro acceso y búsqueda en los contenidos de la Web (por ejemplo explicando el uso de algunos operadores de búsqueda, cómo funcionan los algoritmos de ranking, como mejorar la posición de una página Web en los buscadores o cuáles son las peculiaridades de las arquitecturas computacionales de algunos motores de búsqueda). Finalmente, se propone el desarrollo y pruebas de mecanismos más “inteligentes” de acceso, búsqueda, gestión y recuperación de información y conocimiento contenidos en la Web. Para ello se muestra el uso de técnicas avanzadas de Inteligencia Artificial, en particular aquellas más cercanas a la manipulación del lenguaje natural y al comportamiento humano.
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Schneider, Florian. The User-Generated Nation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190876791.003.0007.

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Chapter 7 turns to user-generated content, social media, and ‘Web 2.0’ technologies in digital China’s message boards and comment sections. The cases of the Nanjing Massacre and the Diaoyu Islands then show that online commentaries often provide a nuanced picture of how to make sense of Sino-Japanese relations, and yet the overarching discursive patterns combine with digital mechanisms such as ‘likes’ and algorithmic popularity rankings to push the discussion into nationalist media scripts. In contrast, China’s microblogging spheres at first sight offer a different story: discussions on Weibo or Weixin are diverse, dynamic, and can have impressive reach. Yet the nature of such social networks ultimately either skews them in favour of a few influential users or moves discussions into the walled gardens of small social groups, making nationalist discourse reverberate through the echo chambers of digital China and contributing to a visceral sense of a shared nationhood.
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Book chapters on the topic "Ranking algorithms"

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Jacob, Riko, Ulrich Meyer, and Laura Toma. "List Ranking." In Encyclopedia of Algorithms, 1117–21. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_592.

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Jacob, Riko, Ulrich Meyer, and Laura Toma. "List Ranking." In Encyclopedia of Algorithms, 1–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27848-8_592-1.

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Kieselmann, Olga, Nils Kopal, and Arno Wacker. "Ranking Cryptographic Algorithms." In Socio-technical Design of Ubiquitous Computing Systems, 151–71. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05044-7_9.

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Brazdil, Pavel, Jan N. van Rijn, Carlos Soares, and Joaquin Vanschoren. "Metalearning Approaches for Algorithm Selection I (Exploiting Rankings)." In Metalearning, 19–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_2.

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SummaryThis chapter discusses an approach to the problem of algorithm selection, which exploits the performance metadata of algorithms (workflows) on prior tasks to generate recommendations for a given target dataset. The recommendations are in the form of rankings of candidate algorithms. The methodology involves two phases. In the first one, rankings of algorithms/workflows are elaborated on the basis of historical performance data on different datasets. These are subsequently aggregated into a single ranking (e.g. average ranking). In the second phase, the average ranking is used to schedule tests on the target dataset with the objective of identifying the best performing algorithm. This approach requires that an appropriate evaluation measure, such as accuracy, is set beforehand. In this chapter we also describe a method that builds this ranking based on a combination of accuracy and runtime, yielding good anytime performance. While this approach is rather simple, it can still provide good recommendations to the user. Although the examples in this chapter are from the classification domain, this approach can be applied to other tasks besides algorithm selection, namely hyperparameter optimization (HPO), as well as the combined algorithm selection and hyperparameter optimization (CASH) problem. As this approach works with discrete data, continuous hyperparameters need to be discretized first.
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Kotthoff, Lars. "Ranking Algorithms by Performance." In Lecture Notes in Computer Science, 16–20. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09584-4_2.

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Even, Guy, and Shakhar Smorodinsky. "Hitting Sets Online and Vertex Ranking." In Algorithms – ESA 2011, 347–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23719-5_30.

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Mathieu, Claire, and Adrian Vladu. "Online Ranking for Tournament Graphs." In Approximation and Online Algorithms, 201–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18318-8_18.

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Vembu, Shankar, and Thomas Gärtner. "Label Ranking Algorithms: A Survey." In Preference Learning, 45–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14125-6_3.

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Rejchel, W. "Generalization Bounds for Ranking Algorithms." In Ensemble Classification Methods with Applicationsin R, 135–39. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119421566.ch7.

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Zhou, Xiao, and Takao Nishizeki. "An efficient algorithm for edge-ranking trees." In Algorithms — ESA '94, 118–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/bfb0049402.

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Conference papers on the topic "Ranking algorithms"

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Cortes, Corinna, Mehryar Mohri, and Ashish Rastogi. "Magnitude-preserving ranking algorithms." In the 24th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1273496.1273518.

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Dunaiski, Marcel, and Willem Visser. "Comparing paper ranking algorithms." In the South African Institute for Computer Scientists and Information Technologists Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2389836.2389840.

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Duhan, Neelam, A. K. Sharma, and Komal Kumar Bhatia. "Page Ranking Algorithms: A Survey." In 2009 IEEE International Advance Computing Conference (IACC 2009). IEEE, 2009. http://dx.doi.org/10.1109/iadcc.2009.4809246.

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Chiuso, Alessandro, Fabio Fagnani, Luca Schenato, and Sandro Zampieri. "Gossip algorithms for distributed ranking." In 2011 American Control Conference. IEEE, 2011. http://dx.doi.org/10.1109/acc.2011.5991301.

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Suri, Sandeep, Arushi Gupta, and Kapil Sharma. "Comparative Study of Ranking Algorithms." In 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE). IEEE, 2019. http://dx.doi.org/10.1109/iccece46942.2019.8941989.

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Wei Gao, Yungang Zhang, Li Liang, and Youming Xia. "Stability analysis for ranking algorithms." In 2010 IEEE International Conference on Information Theory and Information Security (ICITIS). IEEE, 2010. http://dx.doi.org/10.1109/icitis.2010.5689665.

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Azar, Yossi, and Iftah Gamzu. "Ranking with Submodular Valuations." In Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2011. http://dx.doi.org/10.1137/1.9781611973082.81.

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Rafiuddin, S. M. "Ranking of Bangla word graph using graph based ranking algorithms." In 2017 3rd International Conference on Electrical Information and Communication Technology (EICT). IEEE, 2017. http://dx.doi.org/10.1109/eict.2017.8275214.

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Cunha, Tiago, Carlos Soares, and André C. P. L. F. de Carvalho. "A label ranking approach for selecting rankings of collaborative filtering algorithms." In SAC 2018: Symposium on Applied Computing. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3167132.3167418.

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Collins, Michael. "Ranking algorithms for named-entity extraction." In the 40th Annual Meeting. Morristown, NJ, USA: Association for Computational Linguistics, 2001. http://dx.doi.org/10.3115/1073083.1073165.

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Reports on the topic "Ranking algorithms"

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Maeno, Yoshiharu. Epidemiological geographic profiling for a meta-population network. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ser.v1i2.78.

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Epidemiological geographic profiling is a statistical method for making inferences about likely areas of a source from the geographical distribution of patients. Epidemiological geographic profiling algorithms are developed to locate a source from the dataset on the number of new cases for a meta-population network model. It is found from the WHO dataset on the SARS outbreak that Hong Kong remains the most likely source throughout the period of observation. This reasoning is pertinent under the restricted circumstance that the number of reported probable cases in China was missing, unreliable, and incomprehensive. It may also imply that globally connected Hong Kong was more influential as a spreader than China. Singapore, Taiwan, Canada, and the United States follow Hong Kong in the likeliness ranking list
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Hoffman, Jenifer, David Prochnow, Paul Smith, Jonathan Teague, and Douglas Veirs. Updates to Risk Ranking Algorithm for Repackaging Prioritization of LANL Nuclear Material Storage Containers. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1148961.

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Khrushch, Nila, Pavlo Hryhoruk, Tetiana Hovorushchenko, Sergii Lysenko, Liudmyla Prystupa, and Liudmyla Vahanova. Assessment of bank's financial security levels based on a comprehensive index using information technology. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4474.

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The article considers the issues of assessing the level of financial security of the bank. An analysis of existing approaches to solving this problem. A scientific and methodological approach based on the application of comprehensive assessment technology is proposed. The computational algorithm is presented in the form of a four-stage procedure, which contains the identification of the initial data set, their normalization, calculation of the partial composite indexes, and a comprehensive index of financial security. Results have interpretation. Determining the levels of financial security and the limits of the relevant integrated indicator is based on the analysis of the configuration of objects in the two-scale space of partial composite indexes, which is based on the division of the set of initial indicators by content characteristics. The results of the grouping generally coincided with the results of the banks ranking according to the rating assessment of their stability, presented in official statistics. The article presents the practical implementation of the proposed computational procedure. To automate calculations and the possibility of scenario modeling, an electronic form of a spreadsheet was created with the help of form controls. The obtained results allowed us to identify the number of levels of financial security and their boundaries.
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