Journal articles: '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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Abdulrahman, Ayad. "Web Pages Ranking Algorithms: A Survey." Qubahan Academic Journal 1, no. 3 (July 1, 2021): 29–34. http://dx.doi.org/10.48161/qaj.v1n3a79.

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Due to the daily expansion of the web, the amount of information has increased significantly. Thus, the need for retrieving relevant information has also increased. In order to explore the internet, users depend on various search engines. Search engines face a significant challenge in returning the most relevant results for a user's query. The search engine's performance is determined by the algorithm used to rank web pages, which prioritizes the pages with the most relevancy to appear at the top of the result page. In this paper, various web page ranking algorithms such as Page Rank, Time Rank, EigenRumor, Distance Rank, SimRank, etc. are analyzed and compared based on some parameters, including the mining technique to which the algorithm belongs (for instance, Web Content Mining, Web Structure Mining, and Web Usage Mining), the methodology used for ranking web pages, time complexity (amount of time to run an algorithm), input parameters (parameters utilized in the ranking process such as InLink, OutLink, Tag name, Keyword, etc.), and the result relevancy to the user query.

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Li, Gai, Liyang Wang, and Weihua Ou. "Robust Personalized Ranking from Implicit Feedback." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 01 (December 30, 2015): 1659001. http://dx.doi.org/10.1142/s0218001416590011.

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In this paper, we investigate the problem of personalized ranking from implicit feedback (PRIF). It is a more common scenario (e.g. purchase history, click log and page visitation) in recommender systems. The training data are only binary in these problems, reflecting the users’ actions or inactions. One shortcoming of previous PRIF algorithms is noise sensitivity: outliers in training data might bring significant fluctuations in the training process and lead to inaccuracy of the algorithm. In this paper, we propose two robust PRIF algorithms to solve the noise sensitivity problem of existing PRIF algorithms by using the pairwise sigmoid and pairwise fidelity loss functions. These two pairwise loss functions are flexible and can easily be adopted by popular collaborative filtering models such as the matrix factorization (MF) model and the K-nearest-neighbor (KNN) model. A learning process based on stochastic gradient descent with bootstrap sampling is utilized for the optimization. Experiments are conducted on practical datasets containing noisy data points or outliers. Results demonstrate that the proposed algorithms outperform several state-of-the-art one class collaborative filtering (OCCF) algorithms on both the MF and KNN models over different evaluation metrics.

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Davoodi Monfared, M., A. Mohades, and J. Rezaei. "Convex hull ranking algorithm for multi-objective evolutionary algorithms." Scientia Iranica 18, no. 6 (December 2011): 1435–42. http://dx.doi.org/10.1016/j.scient.2011.08.017.

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Ivanov, A. A., and N. P. Yashina. "Big Data Analysis in Multi-Criteria Choice Problems." Моделирование и анализ данных 12, no. 2 (2022): 5–19. http://dx.doi.org/10.17759/mda.2022120201.

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The problem of multi-criteria choice with non-uniform scales of criteria is considered. A model of a multicriteria choice problem is described, the main elements of which are sets of alternatives and quality criteria, as well as algorithms that allow ranking alternatives without prior reduction of the criteria scales to homogeneous ones. Algorithms for constructing aggregated ranking of alternatives are based on the construction of preference matrices by criteria containing information on the degree of superiority of one alternative over another. Propositions are proved that allow ranking alternatives with assessments according to two quality criteria. Algorithms for indexing alternatives are proposed that allow ranking alternatives for an arbitrary number of criteria. The best aggregated ranking is determined by the total distance to the rankings of alternatives by criteria. All algorithms have polynomial computational complexity, which makes it possible to work with large arrays of initial information. A software system for ranking alternatives in problems with big data has been developed. The initial information is stored in Excel tables, which makes it easy to take into account the limitations on the criteria scales. The operation of the software system is demonstrated by the example of choosing the best version of a drone for purchase in order to observe the terrain, shoot it and transmit information to the operator.

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Li, Chang. "Optimizing ranking systems online as bandits." ACM SIGIR Forum 55, no. 2 (December 2021): 1–2. http://dx.doi.org/10.1145/3527546.3527575.

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Ranking system is the core part of modern retrieval and recommender systems, where the goal is to rank candidate items given user contexts. Optimizing ranking systems online means that the deployed system can serve users' requests, e.g., queries in the web search, and optimize the ranking policy by learning from user interactions, e.g., clicks. Bandit is a general online learning framework and can be used in our optimization task. However, due to the unique features of ranking, there are several challenges in designing bandit algorithms for ranking system optimization. In this thesis, we study and propose bandit algorithms for four challenges in optimizing ranking systems online: effectiveness, safety, nonstationarity, and diversification. We first focus on the large-scale online ranker evaluation problem. The challenge is that the number of pair-wise ranker comparisons grows quadratically with respect to the number of rankers. We proposed the merge double Thompson sampling (MergeDTS) method to solve the problem. MergeDTS takes the divide-and-conquer idea in Merge Sort to decrease the complexity and uses the Thompson sampling to increase the effectiveness of pair-wise comparisons. We then address the safety Online Learning to Rank (OLTR) by introducing the BubbleRank algorithm. BubbleRank uses the offline trained ranker, e.g., the production ranker, to obtain the initial ranked list, and then conducts safe online pairwise exploration to improve this list. The safety comes from the fact that BubbleRank explores the ranked lists by randomly exchanging items with their neighbors. Thus, during exploration, low-quality items are hardly shifted at top positions. Non-stationarity widely appears in interactive systems since user preferences are affected by different factors and change over time. It is critical to design algorithms that capture the non-stationarity in OLTR. This thesis provides CascadeDUCB and CascadeSWUCB algorithms to solve the non-stationary OLTR. We derive gap-dependent bounds on their regret and show the theoretical soundness of the proposed algorithms, and then we conduct simulated experiments to validate the empirical effectiveness. Result diversification and relevance ranking are two important aspects in modern recommender systems. Ideal learning algorithms should be able to display a ranked list whose items are relevant and the topics of items are diverse. The last research chapter of the thesis focuses on this challenge and provides the CascadeHybrid algorithm. CascadeHybrid learns from interactive feedback online and trained a ranker, which is a hybrid of a linear function capturing the relevance part and a submodular function responding to the results diversification. Awarded by : University of Amsterdam, Amsterdam, the Netherlands on 4 March 2021. Supervised by : Maarten de Rijke. Available at : https://dare.uva.nl/search?identifier=f043b9b4-e666-48e0-8a6c-7c5431660e17.

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Gao, Wei, and Tianwei Xu. "Stability Analysis of Learning Algorithms for Ontology Similarity Computation." Abstract and Applied Analysis 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/174802.

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Ontology, as a useful tool, is widely applied in lots of areas such as social science, computer science, and medical science. Ontology concept similarity calculation is the key part of the algorithms in these applications. A recent approach is to make use of similarity between vertices on ontology graphs. It is, instead of pairwise computations, based on a function that maps the vertex set of an ontology graph to real numbers. In order to obtain this, the ranking learning problem plays an important and essential role, especiallyk-partite ranking algorithm, which is suitable for solving some ontology problems. A ranking function is usually used to map the vertices of an ontology graph to numbers and assign ranks of the vertices through their scores. Through studying a training sample, such a function can be learned. It contains a subset of vertices of the ontology graph. A good ranking function means small ranking mistakes and good stability. For ranking algorithms, which are in a well-stable state, we study generalization bounds via some concepts of algorithmic stability. We also find that kernel-based ranking algorithms stated as regularization schemes in reproducing kernel Hilbert spaces satisfy stability conditions and have great generalization abilities.

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Li, Jinzhong, and Guanjun Liu. "An Improved LambdaMART Algorithm Based on the Matthew Effect." Mathematical Problems in Engineering 2018 (November 6, 2018): 1–11. http://dx.doi.org/10.1155/2018/3082970.

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Matthew effect is a desirable phenomenon for a ranking model in search engines and recommendation systems. However, most of algorithms of learning to rank (LTR) do not pay attention to Matthew effect. LambdaMART is a well-known LTR algorithm that can be further optimized based on Matthew effect. Inspired by Matthew effect, we distinguish queries with different effectiveness and then assign a higher weight to a query with higher effectiveness. We improve the gradient in the LambdaMART algorithm to optimize the queries with high effectiveness, that is, to highlight the Matthew effect of the produced ranking models. In addition, we propose strategies of evaluating a ranking model and dynamically decreasing the learning rate to both strengthen the Matthew effect of ranking models and improve the effectiveness of ranking models. We use Gini coefficient, mean-variance, quantity statistics, and winning number to measure the performances of the ranking models. Experimental results on multiple benchmark datasets show that the ranking models produced by our improved LambdaMART algorithm can exhibit a stronger Matthew effect and achieve higher effectiveness compared to the original one and other state-of-the-art LTR algorithms.

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Master, Lawrence. "Ranking with Genetics." International Journal of Information Retrieval Research 10, no. 3 (July 2020): 20–34. http://dx.doi.org/10.4018/ijirr.2020070102.

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There are many applications for ranking, including page searching, question answering, recommender systems, sentiment analysis, and collaborative filtering, to name a few. In the past several years, machine learning and information retrieval techniques have been used to develop ranking algorithms and several list wise approaches to learning to rank have been developed. We propose a new method, which we call GeneticListMLE++ and GeneticListNet++, which build on the original ListMLE and ListNet algorithms. Our method substantially improves on the original ListMLE and ListNet ranking approaches by incorporating genetic optimization of hyperparameters, a nonlinear neural network ranking model, and a regularization technique.

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NarayanDas, Nripendra, Ela Kumar, and Sheetal Sheetal. "Approaches of Page Ranking Algorithms: Review." International Journal of Computer Applications 82, no. 2 (November 15, 2013): 31–38. http://dx.doi.org/10.5120/14090-2094.

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Jain, Rekha, and Dr G. N. Purohit. "Page Ranking Algorithms for Web Mining." International Journal of Computer Applications 13, no. 5 (January 12, 2011): 22–25. http://dx.doi.org/10.5120/1775-2448.

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Dhingra, Vandana, and Komal Bhatia. "Comparative Analysis of Ontology Ranking Algorithms." International Journal of Information Technology and Web Engineering 7, no. 3 (July 2012): 55–66. http://dx.doi.org/10.4018/jitwe.2012070104.

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Ontologies are the backbone of knowledge representation on Semantic web. Challenges involved in building ontologies are in terms of time, efforts, skill, and domain specific knowledge. In order to minimize these challenges, one of the major advantages of ontologies is its potential of “reuse,” currently supported by various search engines like Swoogle, Ontokhoj. As the number of ontologies that such search engines like Swoogle, OntoKhoj Falcon can find increases, so will the need increase for a proper ranking method to order the returned lists of ontologies in terms of their relevancy to the query which can save a lot of time and effort. This paper deals with analysis of various ontology ranking algorithms. Based on the analysis of different ontology ranking algorithms, a comparative study is done to find out their relative strengths and limitations based on various parameters which provide a significant research direction in ranking of ontologies in semantic web.

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Chapelle, O., and S. S. Keerthi. "Efficient algorithms for ranking with SVMs." Information Retrieval 13, no. 3 (September 9, 2009): 201–15. http://dx.doi.org/10.1007/s10791-009-9109-9.

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DE QUEIRÓS VIEIRA MARTINS, ERNESTO, MARTA MARGARIDA BRAZ PASCOAL, and JOSÉ LUIS ESTEVES DOS SANTOS. "DEVIATION ALGORITHMS FOR RANKING SHORTEST PATHS." International Journal of Foundations of Computer Science 10, no. 03 (September 1999): 247–61. http://dx.doi.org/10.1142/s0129054199000186.

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The shortest path problem is a classical network problem that has been extensively studied. The problem of determining not only the shortest path, but also listing the K shortest paths (for a given integer K>1) is also a classical one but has not been studied so intensively, despite its obvious practical interest. Two different types of problems are usually considered: the unconstrained and the constrained K shortest paths problem. While in the former no restriction in considered in the definition of a path, in the constrained K shortest paths problem all the paths have to satisfy some condition – for example, to be loopless. In this paper new algorithms are proposed for the uncontrained problem, which compute a super set of the K shortest paths. It is also shown that ranking loopless paths does not hold in general the Optimality Principle and how the proposed algorithms for the unconstrained problem can be adapted for ranking loopless paths.

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PaulSelvan, Mercy, A. Chandra Sekar, and A. Priya Dharshini. "Survey on Web Page Ranking Algorithms." International Journal of Computer Applications 41, no. 19 (March 31, 2012): 1–7. http://dx.doi.org/10.5120/5646-7764.

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Kocbek, S., R. Saetre, G. Stiglic, J. D. Kim, I. Pernek, Y. Tsuruoka, P. Kokol, S. Ananiadou, and J. Tsujii. "AGRA: analysis of gene ranking algorithms." Bioinformatics 27, no. 8 (February 23, 2011): 1185–86. http://dx.doi.org/10.1093/bioinformatics/btr097.

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Crammer, Koby, and Yoram Singer. "Online Ranking by Projecting." Neural Computation 17, no. 1 (January 1, 2005): 145–75. http://dx.doi.org/10.1162/0899766052530848.

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We discuss the problem of ranking instances. In our framework, each instance is associated with a rank or a rating, which is an integer in 1 to k. Our goal is to find a rank-prediction rule that assigns each instance a rank that is as close as possible to the instance's true rank. We discuss a group of closely related online algorithms, analyze their performance in the mistake-bound model, and prove their correctness. We describe two sets of experiments, with synthetic data and with the Each Movie data set for collaborative filtering. In the experiments we performed, our algorithms outperform online algorithms for regression and classification applied to ranking.

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Shahriary, Saeed Reza, Mohsen Shahriari, and Rafidah MD Noor. "A Community-Based Approach for Link Prediction in Signed Social Networks." Scientific Programming 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/602690.

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In signed social networks, relationships among nodes are of the types positive (friendship) and negative (hostility). One absorbing issue in signed social networks is predicting sign of edges among people who are members of these networks. Other than edge sign prediction, one can define importance of people or nodes in networks via ranking algorithms. There exist few ranking algorithms for signed graphs; also few studies have shown role of ranking in link prediction problem. Hence, we were motivated to investigate ranking algorithms availed for signed graphs and their effect on sign prediction problem. This paper makes the contribution of using community detection approach for ranking algorithms in signed graphs. Therefore, community detection which is another active area of research in social networks is also investigated in this paper. Community detection algorithms try to find groups of nodes in which they share common properties like similarity. We were able to devise three community-based ranking algorithms which are suitable for signed graphs, and also we evaluated these ranking algorithms via sign prediction problem. These ranking algorithms were tested on three large-scale datasets: Epinions, Slashdot, and Wikipedia. We indicated that, in some cases, these ranking algorithms outperform previous works because their prediction accuracies are better.

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Osman, Hassab Elgawi. "Variable Ranking for Online Ensemble Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 3 (May 20, 2009): 331–37. http://dx.doi.org/10.20965/jaciii.2009.p0331.

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In proposing, incremental feature selection based on correlation ranking (CR) for classification problems, we develop on-line training using the random forests (RF) algorithm, then evaluate the performance of the combination based on an NIPS 2003 Feature Selection Challenge dataset. Results show that our approach achieves performance comparable to others batch learning algorithms, including RF.

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Damrudi, Masumeh, and Kamal Jadidy Aval. "Ranking and Closest Element Algorithms on Centralized Diamond Architecture." International Journal of Engineering & Technology 7, no. 4.1 (September 12, 2018): 1. http://dx.doi.org/10.14419/ijet.v7i4.1.19480.

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Employing an appropriate algorithm, hardware and technique makes operations easier and faster in today digital world. Searching data which is a fundamental operation in computer science is an important problem in different Areas. An efficient algorithm is useful to search a data element in huge amount of data while information is growing in every second. There are various papers on searching algorithms to find data elements whereas different types of query in different areas of works including position, rank, count and closest element exists. Each of these queries may be useful in different computations. This paper proposed two algorithms of these four types of query employing Centralized Diamond architecture which consume constant execution time.

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Compare, Michele, Michele Bellora, and Enrico Zio. "Aggregation of importance measures for decision making in reliability engineering." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 231, no. 3 (March 9, 2017): 242–54. http://dx.doi.org/10.1177/1748006x17694495.

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This article investigates the aggregation of rankings based on component importance measures to provide the decision maker with a guidance for design or maintenance decisions. In particular, ranking aggregation algorithms of the literature are considered, a procedure for ensuring that the aggregated ranking is compliant with the Condorcet criterion of majority principle is presented and two original ranking aggregation approaches are proposed. Comparisons are made on a case study of an auxiliary feed-water system of a nuclear pressurized water reactor.

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Chien, S., A. Stechert, and D. Mutz. "Efficient Heuristic Hypothesis Ranking." Journal of Artificial Intelligence Research 10 (June 1, 1999): 375–97. http://dx.doi.org/10.1613/jair.615.

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This paper considers the problem of learning the ranking of a set of stochastic alternatives based upon incomplete information (i.e., a limited number of samples). We describe a system that, at each decision cycle, outputs either a complete ordering on the hypotheses or decides to gather additional information (i.e., observations) at some cost. The ranking problem is a generalization of the previously studied hypothesis selection problem - in selection, an algorithm must select the single best hypothesis, while in ranking, an algorithm must order all the hypotheses. The central problem we address is achieving the desired ranking quality while minimizing the cost of acquiring additional samples. We describe two algorithms for hypothesis ranking and their application for the probably approximately correct (PAC) and expected loss (EL) learning criteria. Empirical results are provided to demonstrate the effectiveness of these ranking procedures on both synthetic and real-world datasets.

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Ferreira, Kris J., Sunanda Parthasarathy, and Shreyas Sekar. "Learning to Rank an Assortment of Products." Management Science 68, no. 3 (March 2022): 1828–48. http://dx.doi.org/10.1287/mnsc.2021.4130.

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We consider the product-ranking challenge that online retailers face when their customers typically behave as “window shoppers.” They form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue browsing. We design online learning algorithms for product ranking that maximize the number of customers who engage with the site. Customers’ product preferences and attention spans are correlated and unknown to the retailer; furthermore, the retailer cannot exploit similarities across products, owing to the fact that the products are not necessarily characterized by a set of attributes. We develop a class of online learning-then-earning algorithms that prescribe a ranking to offer each customer, learning from preceding customers’ clickstream data to offer better rankings to subsequent customers. Our algorithms balance product popularity with diversity, the notion of appealing to a large variety of heterogeneous customers. We prove that our learning algorithms converge to a ranking that matches the best-known approximation factors for the offline, complete information setting. Finally, we partner with Wayfair — a multibillion-dollar home goods online retailer — to estimate the impact of our algorithms in practice via simulations using actual clickstream data, and we find that our algorithms yield a significant increase (5–30%) in the number of customers that engage with the site. This paper was accepted by J. George Shanthikumar, Management Science Special Section on Data-Driven Prescriptive Analytics.

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Yang, Anbang, Jiangbo Qian, Huahui Chen, and Yihong Dong. "A Ranking-Based Hashing Algorithm Based on the Distributed Spark Platform." Information 11, no. 3 (March 9, 2020): 148. http://dx.doi.org/10.3390/info11030148.

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With the rapid development of modern society, generated data has increased exponentially. Finding required data from this huge data pool is an urgent problem that needs to be solved. Hashing technology is widely used in similarity searches of large-scale data. Among them, the ranking-based hashing algorithm has been widely studied due to its accuracy and speed regarding the search results. At present, most ranking-based hashing algorithms construct loss functions by comparing the rank consistency of data in Euclidean and Hamming spaces. However, most of them have high time complexity and long training times, meaning they cannot meet requirements. In order to solve these problems, this paper introduces a distributed Spark framework and implements the ranking-based hashing algorithm in a parallel environment on multiple machines. The experimental results show that the Spark-RLSH (Ranking Listwise Supervision Hashing) can greatly reduce the training time and improve the training efficiency compared with other ranking-based hashing algorithms.

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Zhou, Jianye, Yuewen Jiang, and Biqing Huang. "Source identification of infectious diseases in networks via label ranking." PLOS ONE 16, no. 1 (January 14, 2021): e0245344. http://dx.doi.org/10.1371/journal.pone.0245344.

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Background Outbreaks of infectious diseases would cause great losses to the human society. Source identification in networks has drawn considerable interest in order to understand and control the infectious disease propagation processes. Unsatisfactory accuracy and high time complexity are major obstacles to practical applications under various real-world situations for existing source identification algorithms. Methods This study attempts to measure the possibility for nodes to become the infection source through label ranking. A unified Label Ranking framework for source identification with complete observation and snapshot is proposed. Firstly, a basic label ranking algorithm with complete observation of the network considering both infected and uninfected nodes is designed. Our inferred infection source node with the highest label ranking tends to have more infected nodes surrounding it, which makes it likely to be in the center of infection subgraph and far from the uninfected frontier. A two-stage algorithm for source identification via semi-supervised learning and label ranking is further proposed to address the source identification issue with snapshot. Results Extensive experiments are conducted on both synthetic and real-world network datasets. It turns out that the proposed label ranking algorithms are capable of identifying the propagation source under different situations fairly accurately with acceptable computational complexity without knowing the underlying model of infection propagation. Conclusions The effectiveness and efficiency of the label ranking algorithms proposed in this study make them be of practical value for infection source identification.

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Udupi, Prakash Kumar, Vishal Dattana, P. S. Netravathi, and Jitendra Pandey. "Predicting Global Ranking of Universities Across the World Using Machine Learning Regression Technique." SHS Web of Conferences 156 (2023): 04001. http://dx.doi.org/10.1051/shsconf/202315604001.

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Digital transformation in the field of education plays a significant role especially when used for analysis of various teaching and learning parameters to predict global ranking index of the universities across the world. Machine learning is a subset of computer science facilitates machine to learn the data using various algorithms and predict the results. This research explores the Quacquarelli Symonds approach for evaluating global university rankings and develop machine learning models for predicting global rankings. The research uses exploratory data analysis for analysing the dataset and then evaluate machine learning algorithms using regression techniques for predicting the global rankings. The research also addresses the future scope towards evaluating machine learning algorithms for predicting outcomes using classification and clustering techniques.

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Cao, Yang, Xiaotian Xu, and Zhijing Ye. "Crime Busting Model Based on Dynamic Ranking Algorithms." Abstract and Applied Analysis 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/308675.

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This paper proposed a crime busting model with two dynamic ranking algorithms to detect the likelihood of a suspect and the possibility of a leader in a complex social network. Signally, in order to obtain the priority list of suspects, an advanced network mining approach with a dynamic cumulative nominating algorithm is adopted to rapidly reduce computational expensiveness than most other topology-based approaches. Our method can also greatly increase the accuracy of solution with the enhancement of semantic learning filtering at the same time. Moreover, another dynamic algorithm of node contraction is also presented to help identify the leader among conspirators. Test results are given to verify the theoretical results, which show the great performance for either small or large datasets.

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K.G., Srinivasa, Anil Kumar Muppalla, Bharghava Varun A., and Amulya M. "MapReduce Based Information Retrieval Algorithms for Efficient Ranking of Webpages." International Journal of Information Retrieval Research 1, no. 4 (October 2011): 23–37. http://dx.doi.org/10.4018/ijirr.2011100102.

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In this paper, the authors discuss the MapReduce implementation of crawler, indexer and ranking algorithms in search engines. The proposed algorithms are used in search engines to retrieve results from the World Wide Web. A crawler and an indexer in a MapReduce environment are used to improve the speed of crawling and indexing. The proposed ranking algorithm is an iterative method that makes use of the link structure of the Web and is developed using MapReduce framework to improve the speed of convergence of ranking the WebPages. Categorization is used to retrieve and order the results according to the user choice to personalize the search. A new score is introduced in this paper that is associated with each WebPage and is calculated using user’s query and number of occurrences of the terms in the query in the document corpus. The experiments are conducted on Web graph datasets and the results are compared with the serial versions of crawler, indexer and ranking algorithms.

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Chakraborty, Uday Kumar, Kalyanmoy Deb, and Mandira Chakraborty. "Analysis of Selection Algorithms: A Markov Chain Approach." Evolutionary Computation 4, no. 2 (June 1996): 133–67. http://dx.doi.org/10.1162/evco.1996.4.2.133.

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A Markov chain framework is developed for analyzing a wide variety of selection techniques used in genetic algorithms (GAs) and evolution strategies (ESs). Specifically, we consider linear ranking selection, probabilistic binary tournament selection, deterministic s-ary (s = 3,4, …) tournament selection, fitness-proportionate selection, selection in Whitley's GENITOR, selection in (μ, λ)-ES, selection in (μ + λ)-ES, (μ, λ)-linear ranking selection in GAs, (μ + λ)-linear ranking selection in GAs, and selection in Eshelman's CHC algorithm. The analysis enables us to compare and contrast the various selection algorithms with respect to several performance measures based on the probability of takeover. Our analysis is exact—we do not make any assumptions or approximations. Finite population sizes are considered. Our approach is perfectly general, and following the methods of this paper, it is possible to analyze any selection strategy in evolutionary algorithms.

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Li, Lin, Hengfei Wu, Xiujian Hu, and Guanglei Sheng. "Evolutionary Algorithm for Multiobjective Optimization Based on Density Estimation Ranking." Wireless Communications and Mobile Computing 2021 (July 5, 2021): 1–18. http://dx.doi.org/10.1155/2021/4296642.

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In the past few decades, a number of multiobjective evolutionary algorithms (MOEAs) have been proposed in the continue study. As pointed out in some recent studies, the performance of the most existing MOEAs is not promising when solving different shapes of Pareto fronts. To address this issue, this paper proposes an MOEA based on density estimation ranking. The algorithm includes density estimation ranking to shift the reference solution position, calculating the density of candidate solutions and ranking by the estimated density value, to modify the Pareto dominance relation and for handling complicated Pareto front. The result of this ranking can be used as the second selection criterion for environmental selection, and the optimal candidate individual with distribution and diversity information is selected. Experimental results show that the proposed algorithm can solve various types of Pareto fronts, outperformance several state-of-the-art evolutionary algorithms in multiobjective optimization.

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Wang, Yanshan, In-Chan Choi, and Hongfang Liu. "Generalized ensemble model for document ranking in information retrieval." Computer Science and Information Systems 14, no. 1 (2017): 123–51. http://dx.doi.org/10.2298/csis160229042w.

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A generalized ensemble model (gEnM) for document ranking is proposed in this paper. The gEnM linearly combines the document retrieval models and tries to retrieve relevant documents at high positions. In order to obtain the optimal linear combination of multiple document retrieval models or rankers, an optimization program is formulated by directly maximizing the mean average precision. Both supervised and unsupervised learning algorithms are presented to solve this program. For the supervised scheme, two approaches are considered based on the data setting, namely batch and online setting. In the batch setting, we propose a revised Newton?s algorithm, gEnM.BAT, by approximating the derivative and Hessian matrix. In the online setting, we advocate a stochastic gradient descent (SGD) based algorithm-gEnM.ON. As for the unsupervised scheme, an unsupervised ensemble model (UnsEnM) by iteratively co-learning from each constituent ranker is presented. Experimental study on benchmark data sets verifies the effectiveness of the proposed algorithms. Therefore, with appropriate algorithms, the gEnM is a viable option in diverse practical information retrieval applications.

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Tavana, Madjid, Frank LoPinto, and James W. Smither. "A Hybrid Distance-Based Ideal-Seeking Consensus Ranking Model." Journal of Applied Mathematics and Decision Sciences 2007 (August 19, 2007): 1–18. http://dx.doi.org/10.1155/2007/20489.

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Ordinal consensus ranking problems have received much attention in the management science literature. A problem arises in situations where a group of k decision makers (DMs) is asked to rank order n alternatives. The question is how to combine the DM rankings into one consensus ranking. Several different approaches have been suggested to aggregate DM responses into a compromise or consensus ranking; however, the similarity of consensus rankings generated by the different algorithms is largely unknown. In this paper, we propose a new hybrid distance-based ideal-seeking consensus ranking model (DCM). The proposed hybrid model combines parts of the two commonly used consensus ranking techniques of Beck and Lin (1983) and Cook and Kress (1985) into an intuitive and computationally simple model. We illustrate our method and then run a Monte Carlo simulation across a range of k and n to compare the similarity of the consensus rankings generated by our method with the best-known method of Borda and Kendall (Kendall 1962) and the two methods proposed by Beck and Lin (1983) and Cook and Kress (1985). DCM and Beck and Lin's method yielded the most similar consensus rankings, whereas the Cook-Kress method and the Borda-Kendall method yielded the least similar consensus rankings.

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Dzyuba, Vladimir, Matthijs van Leeuwen, Siegfried Nijssen, and Luc De Raedt. "Interactive Learning of Pattern Rankings." International Journal on Artificial Intelligence Tools 23, no. 06 (December 2014): 1460026. http://dx.doi.org/10.1142/s0218213014600264.

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Pattern mining provides useful tools for exploratory data analysis. Numerous efficient algorithms exist that are able to discover various types of patterns in large datasets. Unfortunately, the problem of identifying patterns that are genuinely interesting to a particular user remains challenging. Current approaches generally require considerable data mining expertise or effort from the data analyst, and hence cannot be used by typical domain experts. To address this, we introduce a generic framework for interactive learning of userspecific pattern ranking functions. The user is only asked to rank small sets of patterns, while a ranking function is inferred from this feedback by preference learning techniques. Moreover, we propose a number of active learning heuristics to minimize the effort required from the user, while ensuring that accurate rankings are obtained. We show how the learned ranking functions can be used to mine new, more interesting patterns. We demonstrate two concrete instances of our framework for two different pattern mining tasks, frequent itemset mining and subgroup discovery. We empirically evaluate the capacity of the algorithm to learn pattern rankings by emulating users. Experiments demonstrate that the system is able to learn accurate rankings, and that the active learning heuristics help reduce the required user effort. Furthermore, using the learned ranking functions as search heuristics allows discovering patterns of higher quality than those in the initial set. This shows that machine learning techniques in general, and active preference learning in particular, are promising building blocks for interactive data mining systems.

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Choudhary, Laxmi. "Role of Ranking Algorithms for Information Retrieval." International Journal of Artificial Intelligence & Applications 3, no. 4 (July 31, 2012): 203–20. http://dx.doi.org/10.5121/ijaia.2012.3415.

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Bhullar, Rachna Singh, and Dr Praveen Dhyani. "Experimental study of Web Page Ranking Algorithms." IOSR Journal of Computer Engineering 16, no. 2 (2014): 100–106. http://dx.doi.org/10.9790/0661-1622100106.

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Hughes-Oliver, Jacqueline M. "Assessment of Prediction Algorithms for Ranking Objects." Notices of the American Mathematical Society 66, no. 02 (February 1, 2019): 1. http://dx.doi.org/10.1090/noti1790.

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Sikdar, Sandipan, Animesh Mukherjee, and Matteo Marsili. "Unsupervised ranking of clustering algorithms by INFOMAX." PLOS ONE 15, no. 10 (October 26, 2020): e0239331. http://dx.doi.org/10.1371/journal.pone.0239331.

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Zaks, Shmuel. "Optimal Distributed Algorithms for Sorting and Ranking." IEEE Transactions on Computers C-34, no. 4 (April 1985): 376–79. http://dx.doi.org/10.1109/tc.1985.5009390.

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Borodin, Allan, Gareth O. Roberts, Jeffrey S. Rosenthal, and Panayiotis Tsaparas. "Link analysis ranking: algorithms, theory, and experiments." ACM Transactions on Internet Technology 5, no. 1 (February 2005): 231–97. http://dx.doi.org/10.1145/1052934.1052942.

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You, Gae-won, and Seung-won Hwang. "Search structures and algorithms for personalized ranking." Information Sciences 178, no. 20 (October 2008): 3925–42. http://dx.doi.org/10.1016/j.ins.2008.06.009.

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Sharma, Prem Sagar, Divakar Yadav, and Pankaj Garg. "A systematic review on page ranking algorithms." International Journal of Information Technology 12, no. 2 (February 22, 2020): 329–37. http://dx.doi.org/10.1007/s41870-020-00439-3.

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Journal articles on the topic 'Ranking algorithms'

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