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Статті в журналах з теми "Ranking learning"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

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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Анотація:

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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2

Yu, Hai-Tao, Degen Huang, Fuji Ren, and Lishuang Li. "Diagnostic Evaluation of Policy-Gradient-Based Ranking." Electronics 11, no. 1 (December 23, 2021): 37. http://dx.doi.org/10.3390/electronics11010037.

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Learning-to-rank has been intensively studied and has shown significantly increasing values in a wide range of domains, such as web search, recommender systems, dialogue systems, machine translation, and even computational biology, to name a few. In light of recent advances in neural networks, there has been a strong and continuing interest in exploring how to deploy popular techniques, such as reinforcement learning and adversarial learning, to solve ranking problems. However, armed with the aforesaid popular techniques, most studies tend to show how effective a new method is. A comprehensive comparison between techniques and an in-depth analysis of their deficiencies are somehow overlooked. This paper is motivated by the observation that recent ranking methods based on either reinforcement learning or adversarial learning boil down to policy-gradient-based optimization. Based on the widely used benchmark collections with complete information (where relevance labels are known for all items), such as MSLRWEB30K and Yahoo-Set1, we thoroughly investigate the extent to which policy-gradient-based ranking methods are effective. On one hand, we analytically identify the pitfalls of policy-gradient-based ranking. On the other hand, we experimentally compare a wide range of representative methods. The experimental results echo our analysis and show that policy-gradient-based ranking methods are, by a large margin, inferior to many conventional ranking methods. Regardless of whether we use reinforcement learning or adversarial learning, the failures are largely attributable to the gradient estimation based on sampled rankings, which significantly diverge from ideal rankings. In particular, the larger the number of documents per query and the more fine-grained the ground-truth labels, the greater the impact policy-gradient-based ranking suffers. Careful examination of this weakness is highly recommended for developing enhanced methods based on policy gradient.

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3

Hüllermeier, Eyke, and Johannes Fürnkranz. "Editorial: Preference learning and ranking." Machine Learning 93, no. 2-3 (August 31, 2013): 185–89. http://dx.doi.org/10.1007/s10994-013-5414-z.

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4

Pan, Weike, Qiang Yang, Yuchao Duan, Ben Tan, and Zhong Ming. "Transfer Learning for Behavior Ranking." ACM Transactions on Intelligent Systems and Technology 8, no. 5 (September 27, 2017): 1–23. http://dx.doi.org/10.1145/3057732.

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5

Jiang, Liangxiao. "Learning random forests for ranking." Frontiers of Computer Science in China 5, no. 1 (December 4, 2010): 79–86. http://dx.doi.org/10.1007/s11704-010-0388-5.

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6

Geng, Xiubo, and Xue-Qi Cheng. "Learning multiple metrics for ranking." Frontiers of Computer Science in China 5, no. 3 (May 6, 2011): 259–67. http://dx.doi.org/10.1007/s11704-011-0152-5.

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7

Jiang, Liangxiao, Chaoqun Li, and Zhihua Cai. "Learning decision tree for ranking." Knowledge and Information Systems 20, no. 1 (October 17, 2008): 123–35. http://dx.doi.org/10.1007/s10115-008-0173-z.

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8

Rahangdale, Ashwini, and Shital Raut. "Machine Learning Methods for Ranking." International Journal of Software Engineering and Knowledge Engineering 29, no. 06 (June 2019): 729–61. http://dx.doi.org/10.1142/s021819401930001x.

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Анотація:

Learning-to-rank is one of the learning frameworks in machine learning and it aims to organize the objects in a particular order according to their preference, relevance or ranking. In this paper, we give a comprehensive survey for learning-to-rank. First, we discuss the different approaches along with different machine learning methods such as regression, SVM, neural network-based, evolutionary, boosting method. In order to compare different approaches: we discuss the characteristics of each approach. In addition to that, learning-to-rank algorithms combine with other machine learning paradigms such as semi-supervised learning, active learning, reinforcement learning and deep learning. The learning-to-rank models employ with parallel or big data analytics to review computational and storage advantage. Many real-time applications use learning-to-rank for preference learning. In regard to this, we introduce some representative works. Finally, we highlighted future directions to investigate learning-to-rank methods.

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9

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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Анотація:

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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10

Ochoa, X., and E. Duval. "Relevance Ranking Metrics for Learning Objects." IEEE Transactions on Learning Technologies 1, no. 1 (January 2008): 34–48. http://dx.doi.org/10.1109/tlt.2008.1.

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11

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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Анотація:

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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12

Zhang, Rong, Ming Gao, Xiaofeng He, and Aoying Zhou. "Learning user credibility for product ranking." Knowledge and Information Systems 46, no. 3 (September 30, 2015): 679–705. http://dx.doi.org/10.1007/s10115-015-0880-1.

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13

Wang, Huiling, Lixiang Xu, Xiaofeng Wang, and Bin Luo. "Learning Optimal Seeds for Ranking Saliency." Cognitive Computation 10, no. 2 (December 1, 2017): 347–58. http://dx.doi.org/10.1007/s12559-017-9528-7.

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14

Hüllermeier, Eyke, Johannes Fürnkranz, Weiwei Cheng, and Klaus Brinker. "Label ranking by learning pairwise preferences." Artificial Intelligence 172, no. 16-17 (November 2008): 1897–916. http://dx.doi.org/10.1016/j.artint.2008.08.002.

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15

Ma, Tao, and Ying Tan. "Stock Ranking with Multi-Task Learning." Expert Systems with Applications 199 (August 2022): 116886. http://dx.doi.org/10.1016/j.eswa.2022.116886.

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16

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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Анотація:

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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17

Chen, Shixing, Ming Dong, and Dongxiao Zhu. "Learning and Interpreting Features to Rank." International Journal of Multimedia Data Engineering and Management 9, no. 3 (July 2018): 17–36. http://dx.doi.org/10.4018/ijmdem.2018070102.

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Анотація:

Previously, it was taken for granted that features learned for classification can also be used for ranking. However, ranking problems possess some distinctive properties, e.g., the ordinal class labels, which indicates the necessity of developing new feature learning procedures dedicated for ranking. In this article, the authors propose to use a convolutional neural network (CNN)-based framework, ranking-CNN, for learning and interpreting features to rank. As a case study, the authors propose to analyze, visualize and work to understand the deep aging patterns in human facial images using ranking-CNN. The authors develop a visualization method that can compare the facial appearance and track its changes at different ages through the mapping between 2D images and a 3D face template. The framework provides an innovative way to understand the human facial aging process.

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18

Werner, Tino. "A review on instance ranking problems in statistical learning." Machine Learning 111, no. 2 (November 18, 2021): 415–63. http://dx.doi.org/10.1007/s10994-021-06122-3.

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Анотація:

AbstractRanking problems, also known as preference learning problems, define a widely spread class of statistical learning problems with many applications, including fraud detection, document ranking, medicine, chemistry, credit risk screening, image ranking or media memorability. While there already exist reviews concentrating on specific types of ranking problems like label and object ranking problems, there does not yet seem to exist an overview concentrating on instance ranking problems that both includes developments in distinguishing between different types of instance ranking problems as well as careful discussions about their differences and the applicability of the existing ranking algorithms to them. In instance ranking, one explicitly takes the responses into account with the goal to infer a scoring function which directly maps feature vectors to real-valued ranking scores, in contrast to object ranking problems where the ranks are given as preference information with the goal to learn a permutation. In this article, we systematically review different types of instance ranking problems and the corresponding loss functions resp. goodness criteria. We discuss the difficulties when trying to optimize those criteria. As for a detailed and comprehensive overview of existing machine learning techniques to solve such ranking problems, we systematize existing techniques and recapitulate the corresponding optimization problems in a unified notation. We also discuss to which of the instance ranking problems the respective algorithms are tailored and identify their strengths and limitations. Computational aspects and open research problems are also considered.

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19

Chen, Na, and Viktor K. Prasanna. "Learning to Rank Complex Semantic Relationships." International Journal on Semantic Web and Information Systems 8, no. 4 (October 2012): 1–19. http://dx.doi.org/10.4018/jswis.2012100101.

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Анотація:

This paper presents a novel ranking method for complex semantic relationship (semantic association) search based on user preferences. The authors’ method employs a learning-to-rank algorithm to capture each user’s preferences. Using this, it automatically constructs a personalized ranking function for the user. The ranking function is then used to sort the results of each subsequent query by the user. Query results that more closely match the user’s preferences gain higher ranks. Their method is evaluated using a real-world RDF knowledge base created from Freebase linked-open-data. The experimental results show that the authors’ method significantly improves the ranking quality in terms of capturing user preferences, compared with the state-of-the-art.

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20

Oosterhuis, Harrie. "Learning from user interactions with rankings." ACM SIGIR Forum 54, no. 2 (December 2020): 1–2. http://dx.doi.org/10.1145/3483382.3483402.

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Анотація:

Ranking systems form the basis for online search engines and recommendation services. They process large collections of items, for instance web pages or e-commerce products, and present the user with a small ordered selection. The goal of a ranking system is to help a user find the items they are looking for with the least amount of effort. Thus the rankings they produce should place the most relevant or preferred items at the top of the ranking. Learning to rank is a field within machine learning that covers methods which optimize ranking systems w.r.t. this goal. Traditional supervised learning to rank methods utilize expert-judgements to evaluate and learn, however, in many situations such judgements are impossible or infeasible to obtain. As a solution, methods have been introduced that perform learning to rank based on user clicks instead. The difficulty with clicks is that they are not only affected by user preferences, but also by what rankings were displayed. Therefore, these methods have to prevent being biased by other factors than user preference. This thesis concerns learning to rank methods based on user clicks and specifically aims to unify the different families of these methods. The first part of the thesis consists of three chapters that look at online learning to rank algorithms which learn by directly interacting with users. Its first chapter considers large scale evaluation and shows existing methods do not guarantee correctness and user experience, we then introduce a novel method that can guarantee both. The second chapter proposes a novel pairwise method for learning from clicks that contrasts with the previous prevalent dueling-bandit methods. Our experiments show that our pairwise method greatly outperforms the dueling-bandit approach. The third chapter further confirms these findings in an extensive experimental comparison, furthermore, we also show that the theory behind the dueling-bandit approach is unsound w.r.t. deterministic ranking systems. The second part of the thesis consists of four chapters that look at counterfactual learning to rank algorithms which learn from historically logged click data. Its first chapter takes the existing approach and makes it applicable to top- k settings where not all items can be displayed at once. It also shows that state-of-the-art supervised learning to rank methods can be applied in the counterfactual scenario. The second chapter introduces a method that combines the robust generalization of feature-based models with the high-performance specialization of tabular models. The third chapter looks at evaluation and introduces a method for finding the optimal logging policy that collects click data in a way that minimizes the variance of estimated ranking metrics. By applying this method during the gathering of clicks, one can turn counterfactual evaluation into online evaluation. The fourth chapter proposes a novel counterfactual estimator that considers the possibility that the logging policy has been updated during the gathering of click data. As a result, it can learn much more efficiently when deployed in an online scenario where interventions can take place. The resulting approach is thus both online and counterfactual, our experimental results show that its performance matches the state-of-the-art in both the online and the counterfactual scenario. As a whole, the second part of this thesis proposes a framework that bridges many gaps between areas of online, counterfactual, and supervised learning to rank. It has taken approaches, previously considered independent, and unified them into a single methodology for widely applicable and effective learning to rank from user clicks. Awarded by: University of Amsterdam, Amsterdam, The Netherlands. Supervised by: Maarten de Rijke. Available at: https://hdl.handle.net/11245.1/8ff3aa38-97fb-4d2a-8127-a29a03af4d5c.

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21

Page, Stewart, Kenneth M. Cramer, and Laura Page. "27. The Sophistry of University Rankings: Implications for Learning and Student Welfare." Collected Essays on Learning and Teaching 2 (June 13, 2011): 159. http://dx.doi.org/10.22329/celt.v2i0.3221.

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Анотація:

We present a data-based perspective concerning the recent Maclean’s magazine rankings of Canadian universities, including cluster and other analyses of the 2007 and 2008 data. Canadian universities empirically resemble and relate to each other in a manner different from their formal classification and final rank ordering in the Maclean’s system. Several pitfalls in ranking procedures, related to invalid and unreliable relationships among indices underlying the final ranks, are outlined, along with relevant findings from previous studies. In their present format, although they have become increasingly publicized and promoted, data based on the Maclean’s system are of limited practical use to students. Perhaps more important, ranking exercises have unintended though potentially serious consequences in terms of the intellectual and personal well-being of students.

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22

Lugovyi, Volodymyr, Olena Slyusarenko, and Zhanneta Talanova. "Ranking distribution and formula funding of Ukrainian Universities: the problem of subjectivism and mistrust." International Scientific Journal of Universities and Leadership, no. 10 (December 20, 2020): 35–69. http://dx.doi.org/10.31874/2520-6702-2020-10-2-35-69.

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Анотація:

Domestic practice of University ranking in 2006-2020 and formula funding of higher education institutions in 2019-2020 was analysed in the article taking into account the objectivity, validity, reliability, accuracy, precision, transparency and clarity of the applied mechanisms. It was considered rankings: Compass , National system of ranking assessment of higher education institutions, Top-200 Ukraine, Scopus, External Evaluation Score for contract learning, External Evaluation Score for budget funding of learning, Consolidated ranking, and Ranking of national higher education institutions according to the Government’ criteria, as well as the formula mechanism for public funds distribution between institutions. Taking into account the world ranking experience and using a large array of factual data, it is proved that all past and current Ukrainian rankings, as well as the current funding formula, are affected by the excessive subjectivity, high discrimination of institutions and are not credible. Therefore, these mechanisms disorient stakeholders, citizens, employers, society as a whole regarding the actual state of higher education. The origins of the lack of objectivity, validity, reliability, transparency, clarity and other important characteristics of ranking and formula mechanisms have been identified. The main reason is the dominance of double subjectivism – the subjective selection of subjective criteria and indicators, which leads to manipulative results, inadequate perception and ultimately to distrust. Conceptual principles of overcoming the current crisis situation are proposed. It is argued that ranking and formula criteria and indicators according to their list and weight should primarily meet the best world practice / methodology of objective ranking and the key components of the three-part University mission – 1) education, 2) research, 3) innovation / creativity or service. At the same time, research serves to education modernizing, and innovation / creativity or service – according to its focus on ensuring long-term development or the current complicated functioning. In addition, ranking and formula developments should be tested by experimental exploitation, verified by testing on benchmarks of excellence, and appropriately adjusted to ensure an objective, valid, and reliable diagnosis of the essential characteristics of higher education, its institutions, and its network in Ukraine before their large-scale application. The article calls for attention and caution with the proposed rankings, in particular Ukrainian ones, and at the same time calls for the creation of an adequate national ranking of higher education institutions, which is important for the formation of an effective strategy for higher education development.

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23

Butt, Anila Sahar, Armin Haller, and Lexing Xie. "DWRank: Learning concept ranking for ontology search." Semantic Web 7, no. 4 (May 27, 2016): 447–61. http://dx.doi.org/10.3233/sw-150185.

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24

Jung, Cheolkon, Yanbo Shen, and Licheng Jiao. "Learning to Rank with Ensemble Ranking SVM." Neural Processing Letters 42, no. 3 (October 11, 2014): 703–14. http://dx.doi.org/10.1007/s11063-014-9382-5.

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25

Chen, Hong, Zhibin Pan, and Luoqing Li. "Learning performance of coefficient-based regularized ranking." Neurocomputing 133 (June 2014): 54–62. http://dx.doi.org/10.1016/j.neucom.2013.11.032.

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26

Yanwei Pang, Zhong Ji, Peiguang Jing, and Xuelong Li. "Ranking Graph Embedding for Learning to Rerank." IEEE Transactions on Neural Networks and Learning Systems 24, no. 8 (August 2013): 1292–303. http://dx.doi.org/10.1109/tnnls.2013.2253798.

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27

Li, Changsheng, Qingshan Liu, Jing Liu, and Hanqing Lu. "Ordinal Distance Metric Learning for Image Ranking." IEEE Transactions on Neural Networks and Learning Systems 26, no. 7 (July 2015): 1551–59. http://dx.doi.org/10.1109/tnnls.2014.2339100.

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28

Fan, Yanbo, Baoyuan Wu, Ran He, Bao-Gang Hu, Yong Zhang, and Siwei Lyu. "Groupwise Ranking Loss for Multi-Label Learning." IEEE Access 8 (2020): 21717–27. http://dx.doi.org/10.1109/access.2020.2969677.

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29

Carreras, A. Xavier, B. Lluís Màrquez, and C. Jorge Castro. "Filtering-Ranking Perceptron Learning for Partial Parsing." Machine Learning 60, no. 1-3 (June 2, 2005): 41–71. http://dx.doi.org/10.1007/s10994-005-0917-x.

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30

Li, Guangxia, Peilin Zhao, Tao Mei, Peng Yang, Yulong Shen, Julian Kuiyu Chang, and Steven C. H. Hoi. "Collaborative online ranking algorithms for multitask learning." Knowledge and Information Systems 62, no. 6 (October 15, 2019): 2327–48. http://dx.doi.org/10.1007/s10115-019-01406-6.

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31

Yuan, Li Wei, Lei Su, Yin Zhang, Guang Fang, and Peng Shu. "Cloud-based learning system for answer ranking." Cluster Computing 20, no. 3 (May 12, 2017): 2253–66. http://dx.doi.org/10.1007/s10586-017-0888-2.

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32

Cai, Wenbin, Muhan Zhang, and Ya Zhang. "Active learning for ranking with sample density." Information Retrieval Journal 18, no. 2 (February 24, 2015): 123–44. http://dx.doi.org/10.1007/s10791-015-9250-6.

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33

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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Анотація:

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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34

Kumar, Pawan, Dhanajit Brahma, Harish Karnick, and Piyush Rai. "Deep Attentive Ranking Networks for Learning to Order Sentences." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 8115–22. http://dx.doi.org/10.1609/aaai.v34i05.6323.

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Анотація:

We present an attention-based ranking framework for learning to order sentences given a paragraph. Our framework is built on a bidirectional sentence encoder and a self-attention based transformer network to obtain an input order invariant representation of paragraphs. Moreover, it allows seamless training using a variety of ranking based loss functions, such as pointwise, pairwise, and listwise ranking. We apply our framework on two tasks: Sentence Ordering and Order Discrimination. Our framework outperforms various state-of-the-art methods on these tasks on a variety of evaluation metrics. We also show that it achieves better results when using pairwise and listwise ranking losses, rather than the pointwise ranking loss, which suggests that incorporating relative positions of two or more sentences in the loss function contributes to better learning.

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35

Xiong, Wei, Zhao Wu, Bing Li, and Qiong Gu. "A Constrained Learning Approach to the Prediction of Reliability Ranking for WSN Services." International Journal of Web Services Research 14, no. 3 (July 2017): 33–52. http://dx.doi.org/10.4018/ijwsr.2017070103.

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Wireless Sensor Network Service Applications (WSAs) are playing an important role in Wireless Sensor Network (WSN), which bridge the gap between WSN and existing widely deployed Service-Oriented Architecture (SOA) technologies. Function properties of WSN services are important, which assure correct functionality of WSA. Meanwhile, nonfunctional properties such as reliability might significantly influence the client-perceived quality of WSA. Thus, building high-reliability WSA is a critical research problem. Reliability rankings provide valuable information for making optimal WSN service selection from functionally equivalent service candidates. There existed several methods that can conduct reliability ranking prediction of WSN services. However, it is difficult to evaluate which one is better than another, because those acquire different rankings with different preference functions. This paper proposes a constrained learning prediction of reliability ranking approach for WSN services on past service usage experiences of other WSAs, which can achieve higher accuracy and improve the performance by pruning candidate services. To validate the authors' approach, large-scale experiments are conducted based on a real-world WSN service dataset. The results show that their proposed approach achieves higher prediction accuracy than other approaches.

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36

Khan, Hira, Khairul Anuar Mohammad Shah, Jamshed Khalid, Majed Ageel A. Harnmal, and Anees Janee Ali. "Globalization and University Rankings: Consequences and Prospects." International Journal of Higher Education 9, no. 6 (September 21, 2020): 190. http://dx.doi.org/10.5430/ijhe.v9n6p190.

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This study focuses on the effect of globalization on university ranking and current developments and challenges that HEIs face in the global higher education market. It provides detailed information about the origins of international ranking systems, diversification of university rankings and strategic planning of higher education institutes. Moreover, this study describes the global university classification, continuous exposure to elite universities, neglect of the humanities, arts and the social sciences researches, limited description of methods and indigent metrics. The expected effects on ranking system amid the COVID-19 crisis are mentioned which are widely being discussed by the researchers. The study concludes that there is a threat that universities which are investing time and money in accumulating and using statistics and data for the sake of improvement in their performance for the rankings may destabilize themselves from the development in other areas such as learning, teaching or community involvement.

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37

Zhao, Yulong, Jun Fan, and Lei Shi. "Learning rates for regularized least squares ranking algorithm." Analysis and Applications 15, no. 06 (August 2, 2017): 815–36. http://dx.doi.org/10.1142/s0219530517500063.

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The ranking problem aims at learning real-valued functions to order instances, which has attracted great interest in statistical learning theory. In this paper, we consider the regularized least squares ranking algorithm within the framework of reproducing kernel Hilbert space. In particular, we focus on analysis of the generalization error for this ranking algorithm, and improve the existing learning rates by virtue of an error decomposition technique from regression and Hoeffding’s decomposition for U-statistics.

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38

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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39

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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40

Aithal, P. S., and Suresh Kumar P. M. "Global Ranking and Its Implications in Higher Education." Scholedge International Journal of Business Policy & Governance ISSN 2394-3351 7, no. 3 (August 18, 2020): 25. http://dx.doi.org/10.19085/sijbpg070301.

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Higher Education Institutions try to enhance their competitiveness so as to become distinguished centers of learning and research. Various agencies conduct rankings of institutions independent of each other using different criteria. Although the purpose of ranking is to encourage healthy competition and distinguish the best institution in the interest of the learners to choose, the differences in criteria have cast a lot of confusion in building a parity. Academic performance and allied factors, as well as research, publication, and allied factors, are common to all. Some ranking agencies take into consideration industry-institution collaborations, international outlook, alumni, overall reputation, and even financial stability. This paper aims to attempt a comparison of the ranking methodology adopted by selected prominent Global University Ranking Agencies all over the world and throw light on the positive and negative outcomes of the global ranking. Based on in-depth analysis and critical comments on the limitations of these ranking systems, a generic model for balanced global university ranking is also proposed. Given the fact that nations differ, cultures differ, and the context of higher education itself differ across nations, the study illuminates the fallacy and dangers of segregating all institutions under the same mould.

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41

Jie, Liu, Yuan Kerou, Zhou Jianshe, and Shi Jinsheng. "Study on Ontology Ranking Models Based on the Ensemble Learning." International Journal on Semantic Web and Information Systems 14, no. 2 (April 2018): 138–61. http://dx.doi.org/10.4018/ijswis.2018040107.

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This article describes how more knowledge appears on the Internet than in an ontological form. Displaying results to users precisely when searching is the key issue of the research on ontology retrieval. The considered factors of ontology ranking are not only limited to internal character-matching, but analysis of metadata, including the entities, structures and the relations in ontologies. Currently, existing single feature ranking algorithms focus on the structures, elements and the contents of a certain aspect in ontology, thus, the results are not satisfactory. Combining multiple single-featured models seems to achieve better results, but the objectivity and versatility of models' weights are debatable. Machine learning effectively solves the problem and putting advantages of ranking learning algorithms together is the pressing issue. So we propose ensemble learning strategies to combine different algorithms in ontology ranking. And the ranking result is more satisfied compared to Swoogle and base algorithms.

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42

Ben-Porat, Omer, Itay Rosenberg, and Moshe Tennenholtz. "Convergence of Learning Dynamics in Information Retrieval Games." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1780–87. http://dx.doi.org/10.1609/aaai.v33i01.33011780.

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We consider a game-theoretic model of information retrieval with strategic authors. We examine two different utility schemes: authors who aim at maximizing exposure and authors who want to maximize active selection of their content (i.e., the number of clicks). We introduce the study of author learning dynamics in such contexts. We prove that under the probability ranking principle (PRP), which forms the basis of the current state-of-the-art ranking methods, any betterresponse learning dynamics converges to a pure Nash equilibrium. We also show that other ranking methods induce a strategic environment under which such a convergence may not occur.

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43

Sudirman, Sudirman. "Penerapan Strategi Pembelajaran Billboard Ranking Guna Meningkatkan Prestasi Belajar Sejarah Materi Perkembangan Masyarakat Indonesia Pada Masa Reformasi Pada Siswa Kelas VII A SMP Negeri 1 Cenrana." JIKAP PGSD: Jurnal Ilmiah Ilmu Kependidikan 3, no. 1 (February 4, 2019): 50. http://dx.doi.org/10.26858/jkp.v3i1.8135.

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Abstract. The problems that want to be studied in this study are: (a) How is the increase in student learning achievement by the application of Billboard Ranking learning. (b) What is the influence of the Billboard Ranking learning method on student learning achievement. The purpose of this action research is: (a) Want to know the improvement of student learning achievement after the implementation of Billboard Ranking learning. (b) Want to know the effect of student learning motivation after the implementation of the Billboard Ranking learning method. This study uses action research as many as three rounds. Each round consists of four stages, namely: design, activity and observation, reflection, and refining. The target of this study is students of Class VII A. Academic Year 2016/2017. Data obtained in the form of formative test results, observation sheets of teaching and learning activities. From the results of the analysis, it was found that student learning achievement had increased from cycle I to cycle III, namely, cycle I (59%), cycle II (79%), cycle III (88%). The conclusion of this study is the Billboard Ranking method can have a positive effect on the learning motivation of Class VII A. Students in 2016/2017 lessons, and this learning method can be used as an alternative to learning history.

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44

Goyal, Dev, John Guttag, Zeeshan Syed, Rudra Mehta, Zahoor Elahi, and Mohammed Saeed. "Comparing Precision Machine Learning With Consumer, Quality, and Volume Metrics for Ranking Orthopedic Surgery Hospitals: Retrospective Study." Journal of Medical Internet Research 22, no. 12 (December 1, 2020): e22765. http://dx.doi.org/10.2196/22765.

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Background Patients’ choices of providers when undergoing elective surgeries significantly impact both perioperative outcomes and costs. There exist a variety of approaches that are available to patients for evaluating between different hospital choices. Objective This paper aims to compare differences in outcomes and costs between hospitals ranked using popular internet-based consumer ratings, quality stars, reputation rankings, average volumes, average outcomes, and precision machine learning–based rankings for hospital settings performing hip replacements in a large metropolitan area. Methods Retrospective data from 4192 hip replacement surgeries among Medicare beneficiaries in 2018 in a the Chicago metropolitan area were analyzed for variations in outcomes (90-day postprocedure hospitalizations and emergency department visits) and costs (90-day total cost of care) between hospitals ranked through multiple approaches: internet-based consumer ratings, quality stars, reputation rankings, average yearly surgical volume, average outcome rates, and machine learning–based rankings. The average rates of outcomes and costs were compared between the patients who underwent surgery at a hospital using each ranking approach in unadjusted and propensity-based adjusted comparisons. Results Only a minority of patients (1159/4192, 27.6% to 2078/4192, 49.6%) were found to be matched to higher-ranked hospitals for each of the different approaches. Of the approaches considered, hip replacements at hospitals that were more highly ranked by consumer ratings, quality stars, and machine learning were all consistently associated with improvements in outcomes and costs in both adjusted and unadjusted analyses. The improvement was greatest across all metrics and analyses for machine learning–based rankings. Conclusions There may be a substantive opportunity to increase the number of patients matched to appropriate hospitals across a broad variety of ranking approaches. Elective hip replacement surgeries performed at hospitals where patients were matched based on patient-specific machine learning were associated with better outcomes and lower total costs of care.

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45

Dhole, Yogita. "Deep Learning Search by Social Image Re-ranking." International Journal for Research in Applied Science and Engineering Technology 7, no. 6 (June 30, 2019): 2280–85. http://dx.doi.org/10.22214/ijraset.2019.6382.

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46

Zhang, Xiaoli. "Research on Recommendation Algorithm Based on Ranking Learning." Journal of Electronic Commerce in Organizations 17, no. 1 (January 2019): 60–73. http://dx.doi.org/10.4018/jeco.2019010106.

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After analyzing the logistic regression and support vector machine's limitation, the author has chosen the learning to rank method to solve the problem of news recommendations. The article proposes two news recommendation methods which were based on Bayesian optimization criterion and RankSVM. In addition, the article also proposes two methods to solve the dynamic change of user interest and recommendation novelty and diversity. The experimental results show that the two methods can get ideal results, and the overall performance of the method based on Bayesian optimization criterion is better than that based on RankSVM.

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47

Sigmawaty, Dinda, and Mirna Adriani. "LEARNING WORD RELATEDNESS OVER TIME FOR TEMPORAL RANKING." Jurnal Ilmu Komputer dan Informasi 12, no. 2 (July 8, 2019): 91. http://dx.doi.org/10.21609/jiki.v12i2.745.

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Queries and ranking with temporal aspects gain significant attention in field of Information Retrieval. While searching for articles published over time, the relevant documents usually occur in certain temporal patterns. Given a query that is implicitly time sensitive, we develop a temporal ranking using the important times of query by drawing from the distribution of query trend relatedness over time. We also combine the model with Dual Embedding Space Model (DESM) in the temporal model according to document timestamp. We apply our model using three temporal word embeddings algorithms to learn relatedness of words from news archive in Bahasa Indonesia: (1) QT-W2V-Rank using Word2Vec (2) QT-OW2V-Rank using OrthoTrans-Word2Vec (3) QT-DBE-Rank using Dynamic Bernoulli Embeddings. The highest score was achieved with static word embeddings learned separately over time, called QT-W2V-Rank, which is 66% in average precision and 68% in early precision. Furthermore, studies of different characteristics of temporal topics showed that QT-W2V-Rank is also more effective in capturing temporal patterns such as spikes, periodicity, and seasonality than the baselines.

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48

Yu, Yunlong, Zhong Ji, Jichang Guo, and Yanwei Pang. "Zero-shot learning with regularized cross-modality ranking." Neurocomputing 259 (October 2017): 14–20. http://dx.doi.org/10.1016/j.neucom.2016.06.085.

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49

Long, Bo, Jiang Bian, Olivier Chapelle, Ya Zhang, Yoshiyuki Inagaki, and Yi Chang. "Active Learning for Ranking through Expected Loss Optimization." IEEE Transactions on Knowledge and Data Engineering 27, no. 5 (May 1, 2015): 1180–91. http://dx.doi.org/10.1109/tkde.2014.2365785.

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50

Ahmed, S. Shuaib, B. Purna Chandra Rao, and T. Jayakumar. "A framework for multidimensional learning using multilabel ranking." International Journal of Advanced Intelligence Paradigms 5, no. 4 (2013): 299. http://dx.doi.org/10.1504/ijaip.2013.058301.

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