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Journal articles on the topic 'Ranking learning'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

Guo, Qianyu, Gong Haotong, Xujun Wei, et al. "RankDNN: Learning to Rank for Few-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 728–36. http://dx.doi.org/10.1609/aaai.v37i1.25150.

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This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic. Besides, it provides a new perspective on few-shot learning and is complementary to state-of-the-art methods. The core component of our deep neural network is a simple MLP, which takes as input an image triplet encoded as the difference between two vector-Kronecker products, and outputs a binary relevance ranking order. The proposed RankML

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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 (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

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Hiemstra, Djoerd. "Was Fairness in IR Discussed by Cooper and Robertson in the 1970's?" ACM SIGIR Forum 56, no. 2 (2022): 1–5. http://dx.doi.org/10.1145/3582900.3582924.

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I discuss fairness in Information Retrieval (IR) through the eyes of Cooper and Robertson's probability ranking principle. I argue that unfair rankings may arise from blindly applying the principle without checking whether its preconditions are met. Following this argument, unfair rankings originate from the application of learning-to-rank approaches in cases where they should not be applied according to the probability ranking principle. I use two examples to show that fairer rankings may also be more relevant than rankings that are based on the probability ranking principle.

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Yu, Hai-Tao, Degen Huang, Fuji Ren, and Lishuang Li. "Diagnostic Evaluation of Policy-Gradient-Based Ranking." Electronics 11, no. 1 (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

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Poplavskyi, Mykhailo. "University Ranking As an Education Quality Assessment Tool." Ukrainian Information Space, no. 2(8) (November 15, 2021): 16–38. https://doi.org/10.31866/2616-7948.2(8).2021.245787.

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The purpose of the article is a comprehensive analysis of the ranking methodology of leading international and European University rankings. The research methodology is based on abstraction use, analysis, and synthesis methods, which have provided theoretical comprehension for the phenomenon of world universities ranking. The scientific novelty of the findings involves proposal development for upgrading International University Rankings. Based on the analysis of rates, indicators, and weight assignment of the most influential International University Rankings in the world (ARWU, THE, QS World

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Sumanth Reddy, Shiva, Anil Kumar B, Jahnavi S, Manjunath D R, Girish N, and Nandini C. "Enhancing Academic Excellence through Autonomy: A Data-Driven Analysis of NIRF Rankings in South Indian Higher Educational Institutions." Journal of Hacking Techniques, Digital Crime Prevention and Computer Virology 1, no. 3 (2025): 36–49. https://doi.org/10.46610/johtdcpcv.2024.v01i03.004.

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Published as part of the research "Impact on NIRF Rankings", this study examines how colleges in Andhra Pradesh, Tamil Nadu, Kerala, and Telangana are performing regarding their autonomous status under the National Institutional Ranking Framework (NIRF). Using a detailed dataset from 2016–2023, the study employs advanced statistical techniques, including descriptive statistics, paired t-tests, Wilcoxonsigned-rank tests, and multiple regression models to uncover differences in rankings of NIRF before and after autonomy.Time series analysis with trend lines and moving averages can identify signi

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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 (2017): 1–23. http://dx.doi.org/10.1145/3057732.

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

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

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

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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 paradig

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

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

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Ferreira, Kris J., Sunanda Parthasarathy, and Shreyas Sekar. "Learning to Rank an Assortment of Products." Management Science 68, no. 3 (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

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Basireddy, Chandana Sri, Vishwanth Kumar Goud Cheruku, Prabadevi B, Sivakumar Rajagopal, and Rahul Soangra. "Hybrid prediction models for assessing the Higher Education Institutions Performance in QS World Institution Rankings." F1000Research 13 (December 17, 2024): 1529. https://doi.org/10.12688/f1000research.155847.1.

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Background Quality Education is one of the primary requirements for the best survival. Pursuing higher education in a highly reputed institutions makes much difference in shaping the career of the individual. As many ranking and accreditation boards for higher education institutions like NAAC is prevalent, World ranking distinguishes institution reputation globally. The QS World University Ranking is a vital gauge for learners, educators, and institutions all over the world, allowing them to analyze and compare the quality and reputation of higher education. Predicting these rankings is diffic

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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

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Master, Lawrence. "Ranking with Genetics." International Journal of Information Retrieval Research 10, no. 3 (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 b

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Belmecheri, Nassim, Noureddine Aribi, Nadjib Lazaar, Yahia Lebbah, and Samir Loudni. "Boosting the Learning for Ranking Patterns." Algorithms 16, no. 5 (2023): 218. http://dx.doi.org/10.3390/a16050218.

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Pattern mining is a valuable tool for exploratory data analysis, but identifying relevant patterns for a specific user is challenging. Various interestingness measures have been developed to evaluate patterns, but they may not efficiently estimate user-specific functions. Learning user-specific functions by ranking patterns has been proposed, but this requires significant time and training samples. In this paper, we present a solution that formulates the problem of learning pattern ranking functions as a multi-criteria decision-making problem. Our approach uses an analytic hierarchy process (A

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18

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

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

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Osman, Hassab Elgawi. "Variable Ranking for Online Ensemble Learning." Journal of Advanced Computational Intelligence and Intelligent Informatics 13, no. 3 (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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Zhang, Rong, Ming Gao, Xiaofeng He, and Aoying Zhou. "Learning user credibility for product ranking." Knowledge and Information Systems 46, no. 3 (2015): 679–705. http://dx.doi.org/10.1007/s10115-015-0880-1.

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

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

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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 a

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

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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 rank

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Gayathiri V, Rooban Reyeash S, and Ashish J. "AI-Powered Mobile App for Detecting Fraudulent Search Rankings." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 04 (2025): 1662–65. https://doi.org/10.47392/irjaeh.2025.0236.

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In the digital era, search engine rankings play a crucial role in shaping online visibility and user engagement. However, fraudulent practices such as search ranking manipulation, click fraud, and SEO poisoning have become increasingly prevalent, compromising the credibility of search results. This project introduces an AI-powered mobile application designed to detect fraudulent search rankings and ensure fair visibility for legitimate content. The system leverages deep learning algorithms and data analytics to identify anomalies in search patterns, traffic sources, and keyword manipulation te

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Rai Saba, Mahitri Wiyani, and Titin Sutarti. "Leaving Performance-Oriented Goal (Ranking) Based Education To Improve Student Learning Motivation." International Journal of Multidisciplinary Sciences 2, no. 3 (2024): 272–82. http://dx.doi.org/10.37329/ijms.v2i3.2320.

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Society believes that ranking can be extrinsic motivation for students to improve learning performance, achievement, and student achievement. Ranking also makes it easier for schools to find out the quality of schools, formulate policies, and select colleges to enter. On the other hand, ranking triggers "competition" not only among students, but also between parents. The pressure that children feel to always "achieve" in this case to win in class, is also getting higher. However, reflecting on the quality of Indonesian education, the rankings did not function as expected. This can be seen from

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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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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 ac

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

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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 experim

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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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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 incre

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Wu, Yuehong, Bowen Lu, Lin Tian, and Shangsong Liang. "Learning to Co-Embed Queries and Documents." Electronics 11, no. 22 (2022): 3694. http://dx.doi.org/10.3390/electronics11223694.

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Learning to Rank (L2R) methods that utilize machine learning techniques to solve the ranking problems have been widely studied in the field of information retrieval. Existing methods usually concatenate query and document features as training input, without explicit understanding of relevance between queries and documents, especially in pairwise based ranking approach. Thus, it is an interesting question whether we can devise an algorithm that effectively describes the relation between queries and documents to learn a better ranking model without incurring huge parameter costs. In this paper,

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

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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 learn

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Wisaeng, Kittipol, and Benchalak Muangmeesri. "University Rankings Prediction Using Hybrid Feature Selection Based on Machine Learning Methods." International Journal of Analysis and Applications 23 (May 8, 2025): 112. https://doi.org/10.28924/2291-8639-23-2025-112.

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This study presents a novel approach to predicting university rankings using hybrid feature selection and machine learning techniques. It identifies critical performance factors that affect ranking accuracy using the Times Higher Education (THE) dataset, which includes data from 1,904 universities. A Max-Min normalization method and an artificial neural network were applied to preprocess the data. Then, a hybrid feature selection method, combining statistical and machine learning techniques, was used to determine the optimal feature subsets. Several prediction models, including linear regressi

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Frick, Mira, Ryota Iijima, and Yuhta Ishii. "Welfare Comparisons for Biased Learning." American Economic Review 114, no. 6 (2024): 1612–49. http://dx.doi.org/10.1257/aer.20210410.

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We study robust welfare comparisons of learning biases (misspecified Bayesian and some forms of non-Bayesian updating). Given a true signal distribution, we deem one bias more harmful than another if it yields lower objective expected payoffs in all decision problems. We characterize this ranking in static and dynamic settings. While the static characterization compares posteriors signal by signal, the dynamic characterization employs an “efficiency index” measuring how fast beliefs converge. We quantify and compare the severity of several well-documented biases. We also highlight disagreement

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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 (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 o

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Yang, Hua, and Teresa Gonçalves. "MultiLTR: Text Ranking with a Multi-Stage Learning-to-Rank Approach." Information 16, no. 4 (2025): 308. https://doi.org/10.3390/info16040308.

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The division of retrieval into multiple stages has evolved to balance efficiency and effectiveness among various ranking models. Faster but less accurate models are used to retrieve results from the entire corpus. Slower yet more precise models refine the ranking within the top candidate list. This study proposes a multi-stage learning-to-rank (MultiLTR) method. MultiLTR applies learning-to-rank techniques across multiple stages. It incorporates text from different fields such as titles, body content, and abstracts to produce a more comprehensive and accurate ranking. MultiLTR iteratively refi

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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 (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

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Fu, Zheren, Yan Li, Zhendong Mao, Quan Wang, and Yongdong Zhang. "Deep Metric Learning with Self-Supervised Ranking." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1370–78. http://dx.doi.org/10.1609/aaai.v35i2.16226.

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Deep metric learning aims to learn a deep embedding space, where similar objects are pushed towards together and different objects are repelled against. Existing approaches typically use inter-class characteristics, e.g. class-level information or instance-level similarity, to obtain semantic relevance of data points and get a large margin between different classes in the embedding space. However, the intra-class characteristics, e.g. local manifold structure or relative relationship within the same class, are usually overlooked in the learning process. Hence the data structure cannot be fully

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

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

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

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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 (2013): 1292–303. http://dx.doi.org/10.1109/tnnls.2013.2253798.

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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 (2015): 1551–59. http://dx.doi.org/10.1109/tnnls.2014.2339100.

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

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Li, Guangxia, Peilin Zhao, Tao Mei, et al. "Collaborative online ranking algorithms for multitask learning." Knowledge and Information Systems 62, no. 6 (2019): 2327–48. http://dx.doi.org/10.1007/s10115-019-01406-6.

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

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Manisha, Chaudhari, K. Prajapati Niranjan, and M. Maliya Rama. "Opinion Based Ranking System using Machine Learning." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 5 (2020): 360–63. https://doi.org/10.35940/ijeat.E9557.069520.

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The rapid increase in the online services in the recent years. Everyone sending feedback/review after used particular services. For unstructured data it released active countless opportunity ties and challenges for data mining research. This paper is especially for reviews of hotels which are given by various hotels visitors. Reviews are posted as a comment only. It is difficult to identify the positive & negative review. We used dataset of different hotels and perform sentiment analysis process. For classification word to Vec Algorithm is being used. For the positive and negative review c

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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 on

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