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Relevant bibliographies by topics / Classical rating prediction methods

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Academic literature on the topic 'Classical rating prediction methods'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Classical rating prediction methods"

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S., Babeetha, Muruganantham B., Ganesh Kumar S., and Murugan A. "An enhanced kernel weighted collaborative recommended system to alleviate sparsity." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 447–54. https://doi.org/10.11591/ijece.v10i1.pp447-454.

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User Reviews in the form of ratings giving an opportunity to judge the user interest on the available products and providing a chance to recommend new similar items to the customers. Personalized recommender techniques placing vital role in this grown ecommerce century to predict the users" interest. Collaborative Filtering (CF) system is one of the widely used democratic recommender system where it completely rely on user ratings to provide recommendations for the users. In this paper, an enhanced Collaborative Filtering system is proposed using Kernel Weighted K-means Clustering (KWKC) appro

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Wang, Jinze, Yongli Ren, Jie Li, and Ke Deng. "The Footprint of Factorization Models and Their Applications in Collaborative Filtering." ACM Transactions on Information Systems 40, no. 4 (2022): 1–32. http://dx.doi.org/10.1145/3490475.

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Factorization models have been successfully applied to the recommendation problems and have significant impact to both academia and industries in the field of Collaborative Filtering ( CF ). However, the intermediate data generated in factorization models’ decision making process (or training process , footprint ) have been overlooked even though they may provide rich information to further improve recommendations. In this article, we introduce the concept of Convergence Pattern, which records how ratings are learned step-by-step in factorization models in the field of CF. We show that the con

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Baillères, Henri, Gary Hopewell, Geoff Boughton, and Loic Brancheriau. "Strength and stiffness assessment technologies for improving grading effectiveness of radiata pine wood." BioResources 7, no. 1 (2012): 1264–82. http://dx.doi.org/10.15376/biores.7.1.1264-1282.

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This work was designed to provide the Australian structural radiata pine processing industry with some indications for improving stress grading methods and/or technologies to give an increase in structural grade yields, and significantly reduce processing costs without compromising product quality. To achieve this, advanced statistical techniques were used in conjunction with state-of-the-art property measurement systems applied to the same sample of sawn timber. Acoustic vibration analyses were conducted on green and dry boards. Raw data from existing in-line systems was captured on the same

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Barchet, Alice Vivien, Johanna M. Rimmele, and Claire Pelofi. "TenseMusic: An automatic prediction model for musical tension." PLOS ONE 19, no. 1 (2024): e0296385. http://dx.doi.org/10.1371/journal.pone.0296385.

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The perception of tension and release dynamics constitutes one of the essential aspects of music listening. However, modeling musical tension to predict perception of listeners has been a challenge to researchers. Seminal work demonstrated that tension is reported consistently by listeners and can be accurately predicted from a discrete set of musical features, combining them into a weighted sum of slopes reflecting their combined dynamics over time. However, previous modeling approaches lack an automatic pipeline for feature extraction that would make them widely accessible to researchers in

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Trusova, L. A. "Optimization of the prognosis of postoperative complications in patients with breast cancer." Bulletin of the Medical Institute "REAVIZ" (REHABILITATION, DOCTOR AND HEALTH) 15, no. 1 (2025): 89–97. https://doi.org/10.20340/vmi-rvz.2025.1.clin.9.

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Background. Breast cancer (BC) is the most prevalent malignant neoplasm among women, requiring surgical treatment in early stages. Despite the implementation of breast-conserving surgeries, the prevention of early postoperative wound complications remains a significant challenge, as their development leads to delays in adjuvant treatment, prolonged recovery periods, and deterioration in patients' quality of life. Objective: to optimize the prediction of postoperative complications in breast cancer patients by developing a novel surgical approach and a method for predicting wound complications

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Sheridan, Paul, Mikael Onsjö, Claudia Becerra, Sergio Jimenez, and George Dueñas. "An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise." Future Internet 11, no. 9 (2019): 182. http://dx.doi.org/10.3390/fi11090182.

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Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be dif

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Boots, R. J., J. Lipman, R. Bellomo, D. Stephens, and R. F. Heller. "Predictors of Physician Confidence to Diagnose Pneumonia and Determine Illness Severity in Ventilated Patients. Australian and New Zealand Practice in Intensive Care (ANZPIC II)." Anaesthesia and Intensive Care 33, no. 1 (2005): 112–19. http://dx.doi.org/10.1177/0310057x0503300117.

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The manner in which elements of clinical history, physical examination and investigations influence subjectively assessed illness severity and outcome prediction is poorly understood. This study investigates the relationship between clinician and objectively assessed illness severity and the factors influencing clinician's diagnostic confidence and illness severity rating for ventilated patients with suspected pneumonia in the intensive care unit (ICU). A prospective study of fourteen ICUs included all ventilated admissions with a clinical diagnosis of pneumonia. Data collection included pneum

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Ye, Fan, Xiaobo Lu, Hongwei Li, and Zhenyu Chen. "Transfer learning from rating prediction to Top-k recommendation." PLOS ONE 19, no. 3 (2024): e0300240. http://dx.doi.org/10.1371/journal.pone.0300240.

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Recommender system has made great strides in two major research fields, rating prediction and Top-k recommendation. In essence, rating prediction is a regression task, which aims to predict users scores on other items, while Top-k is a classification task selecting the items that users have the most potential to interact with. Both characterize users and items, but the optimization of parameters varies widely for their respective tasks. Inspired by the idea of transfer learning, we consider extracting the information learned from rating prediction models for serving for Top-k tasks. To this en

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Wang, Bingkun, Bing Chen, Li Ma, and Gaiyun Zhou. "User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix." Information 10, no. 1 (2018): 1. http://dx.doi.org/10.3390/info10010001.

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With the explosive growth of product reviews, review rating prediction has become an important research topic which has a wide range of applications. The existing review rating prediction methods use a unified model to perform rating prediction on reviews published by different users, ignoring the differences of users within these reviews. Constructing a separate personalized model for each user to capture the user’s personalized sentiment expression is an effective attempt to improve the performance of the review rating prediction. The user-personalized sentiment information can be obtained n

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Wang, Bingkun, Yongfeng Huang, and Xing Li. "Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating." Computational Intelligence and Neuroscience 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/5968705.

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E-commerce develops rapidly. Learning and taking good advantage of the myriad reviews from online customers has become crucial to the success in this game, which calls for increasingly more accuracy in sentiment classification of these reviews. Therefore the finer-grained review rating prediction is preferred over the rough binary sentiment classification. There are mainly two types of method in current review rating prediction. One includes methods based on review text content which focus almost exclusively on textual content and seldom relate to those reviewers and items remarked in other re

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Dissertations / Theses on the topic "Classical rating prediction methods"

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Gangolf, Claude [Verfasser], and Günter [Akademischer Betreuer] Schmidt. "Models and methods for automated credit rating prediction / Claude Gangolf. Betreuer: Günter Schmidt." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2016. http://d-nb.info/1104178249/34.

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Swiss, Jane Marie. "A study of effective assessment methods for beginning teacher competency." Virtual Press, 1997. http://liblink.bsu.edu/uhtbin/catkey/1063423.

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The focus of the present study was to determine the most effective assessment method to demonstrate beginning teacher competency as perceived by school administrators, teacher education faculty, and practicing teachers in Indiana. Earlier studies examined the compelling reasons why traditional assessments (such as Praxis I and II) were developed and the arguments that supported or negated their continued use. Research relating to nontraditional or alternative assessment (Assessment Portfolio) uncovered a different perspective toward teacher evaluation. The literature produced examples of portf

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Su, Ying-Ju, and 蘇盈如. "A Comparison of Rating Prediction Methods based on Review Mining and User Preference Factor Analysis." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/fd3m57.

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碩士<br>國立交通大學<br>資訊管理研究所<br>106<br>Online review websites not only allow users to share business information and consumer experience, but also the ability to rate, review and influence one another. They help users decide whether to buy products or visit business stores indirectly. However, users are difficult to filter out useful information efficiently due to the overload from a large amount of review information. Accordingly, in order to make accurate predictions for personalizing recommendation systems, it is an important issue to analyze user preferences and predict user ratings by analyzin

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"Analysis Methods for Post Occupancy Evaluation of Energy-Use in High Performance Buildings Using Short-Term Monitoring." Master's thesis, 2011. http://hdl.handle.net/2286/R.I.9245.

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abstract: The green building movement has been an effective catalyst in reducing energy demands of buildings and a large number of `green' certified buildings have been in operation for several years. Whether these buildings are actually performing as intended, and if not, identifying specific causes for this discrepancy falls into the general realm of post-occupancy evaluation (POE). POE involves evaluating building performance in terms of energy-use, indoor environmental quality, acoustics and water-use; the first aspect i.e. energy-use is addressed in this thesis. Normally, a full year or m

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Books on the topic "Classical rating prediction methods"

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Chen, Min, J. Michael Dunn, Amos Golan, and Aman Ullah, eds. Advances in Info-Metrics. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190636685.001.0001.

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Info-metrics is a framework for modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is an interdisciplinary framework situated at the intersection of information theory, statistical inference, and decision-making under uncertainty. In a recent book on the Foundations of Info-Metrics, Golan (OUP, 2018) provides the theoretical underpinning of info-metrics and the necessary tools and building blocks for using that framework. This volume complements Golan’s book and expands on the series of studies on the classical maximum entropy and Bayesian me

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Book chapters on the topic "Classical rating prediction methods"

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Zukanović, Milica, Pavle Milošević, Ana Poledica, and Aleksandra Vučičević. "An Approach to Corporate Credit Rating Prediction Using Computational Intelligence-Based Methods." In Sustainable Business Management and Digital Transformation: Challenges and Opportunities in the Post-COVID Era. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18645-5_6.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Bayesian and Classical Prediction Models for Categorical and Count Data." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_7.

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AbstractIn this chapter, we explain, under a Bayesian framework, the fundamentals and practical issues for implementing genomic prediction models for categorical and count traits. First, we derive the Bayesian ordinal model and exemplify it with plant breeding data. These examples were implemented in the library BGLR. We also derive the ordinal logistic regression. The fundamentals and practical issues of penalized multinomial logistic regression and penalized Poisson regression are given including several examples illustrating the use of the glmnet library. All the examples include main effec

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Guerdan, Luke, Peng Sun, Connor Rowland, et al. "Deep Learning vs. Classical Machine Learning: A Comparison of Methods for Fluid Intelligence Prediction." In Adolescent Brain Cognitive Development Neurocognitive Prediction. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31901-4_3.

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Micsonai, András, Éva Bulyáki, and József Kardos. "BeStSel: From Secondary Structure Analysis to Protein Fold Prediction by Circular Dichroism Spectroscopy." In Methods in Molecular Biology. Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0892-0_11.

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Abstract Far-UV circular dichroism (CD) spectroscopy is a classical method for the study of the secondary structure of polypeptides in solution. It has been the general view that the α-helix content can be estimated accurately from the CD spectra. However, the technique was less reliable to estimate the β-sheet contents as a consequence of the structural variety of the β-sheets, which is reflected in a large spectral diversity of the CD spectra of proteins containing this secondary structure component. By taking into account the parallel or antiparallel orientation and the twist of the β-sheet

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Przybyszewski, Krzysztof. "AI Methods for a Prediction of the Pedagogical Efficiency Factors for Classical and e-Learning System." In Artifical Intelligence and Soft Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13232-2_78.

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Witt, Nicolas, Mark Deutel, Jakob Schubert, Christopher Sobel, and Philipp Woller. "Energy-Efficient AI on the Edge." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_19.

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AbstractThis chapter shows methods for the resource-optimized design of AI functionality for edge devices powered by microprocessors or microcontrollers. The goal is to identify Pareto-optimal solutions that satisfy both resource restrictions (energy and memory) and AI performance. To accelerate the design of energyefficient classical machine learning pipelines, an AutoML tool based on evolutionary algorithms is presented, which uses an energy prediction model from assembly instructions (prediction accuracy 3.1%) to integrate the energy demand into a multiobjective optimization approach. For t

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Zhang, Yecheng, Qimin Zhang, Yuxuan Zhao, Yunjie Deng, Feiyang Liu, and Hao Zheng. "Artificial Intelligence Prediction of Urban Spatial Risk Factors from an Epidemic Perspective." In Computational Design and Robotic Fabrication. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8637-6_18.

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AbstractFrom the epidemiological perspective, previous research methods of COVID-19 are generally based on classical statistical analysis. As a result, spatial information is often not used effectively. This paper uses image-based neural networks to explore the relationship between urban spatial risk and the distribution of infected populations, and the design of urban facilities. We take the Spatio-temporal data of people infected with new coronary pneumonia before February 28 in Wuhan in 2020 as the research object. We use kriging spatial interpolation technology and core density estimation

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Yuan, Jiaxin, Daniela Grigori, and Han van der Aa. "Enhancing Predictive Process Monitoring Using Semantic Information." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82225-4_22.

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Abstract Predictive Process Monitoring (PPM) leverages historical data to forecast information about ongoing business processes. Recent methods have utilized advanced deep learning and classical machine learning models. However, the role of semantic information that can be extracted from event logs has been underexplored, although such information has been demonstrated to have significant advantages for other process mining tasks, such as anomaly detection. Therefore, this paper proposes a novel mechanism that aims to exploit semantic information for PPM, particularly by extracting information

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Wu, Xinyang, Philipp Wagner, and Marco F. Huber. "Quantification of Uncertainties in Neural Networks." In New Digital Work. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26490-0_16.

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AbstractArtificial neural networks only compute point estimates and thus, do not provide the user with a proper decision space. In high-risk use cases, the confidence of the neural network is an important support for decision-making. Bayesian neural networks extend classical deep neural networks with a probability component and allow the user to assess the probability distribution over the prediction. Due to the large number of parameters to be learned, the calculation of the predictive probability can only be performed approximately in practice. In recent years, many methods have been develop

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Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Numerical Experiments and Real World Cases." In Regularized System Identification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_9.

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AbstractThis chapter collects some numerical experiments to test the performance of kernel-based approaches for discrete-time linear system identification. Using Monte Carlo simulations, we will compare the performance of kernel-based methods with the classical PEM approaches described in Chap. 10.1007/978-3-030-95860-2_2. Simulated and real data are included, concerning a robotic arm, a hairdryer and a problem of temperature prediction. We conclude the chapter by introducing the so-called multi-task learning where several functions (tasks) are simultaneously estimated. This problem is signifi

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Conference papers on the topic "Classical rating prediction methods"

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Erwee, Bianca. "Novel Methodology for the Prediction of Rotorcraft Detectability using Classic Signal Detection Theory." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-223.

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Survivability in the future operating environment is becoming more challenging as threat systems evolve and become more sophisticated. The ability to tailor and manage signatures will be one of the key methods to improve survivability, allowing operators to minimise detection and maximise the effectiveness of countermeasures. This paper presents the findings of an investigation into the application of classical Signal Detection Theory (SDT) to the aural detectability of helicopter noise signatures, considering human auditory capabilities. The paper has thus developed a novel methodology, appli

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Tonetti, Lorenzo G., and Ruy de Sousa. "Computational Intelligence Applied to the Mathematical Modeling of the Esterification of Fatty Acids with Sugars." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.190968.

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The mathematical modeling of enzymatic reactors for esterification of fatty acids with sugars in the production of biosurfactants has been a useful tool for studying and optimizing the process. In particular, artificial neural networks and fuzzy systems emerge as promising methods for developing models for those processes. In this work, regarding artificial neural networks application, coupling of networks to reactor mass balances was considered in hybrid models to infer reactant concentrations over time. Computationally, an algorithm was constructed incorporating material balances, neural rea

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Beqiraj, Klajdi, Andrea Perrone, Marco Sanguineti, and Gianluca Ricci. "Advantages of Machine Learning Methods in Aerodynamic Blade Optimization." In ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/gt2023-102481.

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Abstract The present paper describes the advantages of using Machine Learning methods within the aerodynamic optimization of blades, highlighting the benefits of such techniques in terms of both design time, and expected performance. The case study considered is the NASA Rotor 37. The automatic parameterization of entire datasets through the use of variational autoencoders, a specific type of Neural Network, is explained and discussed. The autoencoder latent parameters describe the blade 3D geometry and can be used as an alternative to the standard geometric parameters in describing the shape

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Bliss, Donald, Linda Franzoni, and Krista Michalis. "Characterization of High Frequency Radiation From Panels Subject to Broadband Excitation." In ASME 2008 Noise Control and Acoustics Division Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/ncad2008-73063.

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In the high frequency limit, a vibrating panel subject to spatially-random temporally-broadband forcing is shown to have broadband power and directivity properties that can be expressed in simple analytical terms by a limited set of parameters. A lightly-loaded fixed-fixed membrane with a distribution of broadband uncorrelated drive points is analyzed. The theory is developed using classical modal methods and asymptotic modal analysis, assuming small damping. The power and directivity of the radiated pressure field are characterized in terms of structural wave Mach number, damping ratio, and d

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Michael, Ogbu, Ogunleye Adewale, and Mbanefo Edith. "Identification of Recovery Improvement Opportunities for a Brownfield Deepwater Asset Using Analytic Methods." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/221731-ms.

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Abstract Identifying opportunities to improve hydrocarbon recovery in a brownfield is usually preceded by an assessment of the effectiveness of the development strategy and the gap to potential ultimate recoverable. In recent times, the use of numerical reservoir modeling techniques has dominated the assessment of reservoir performance and prediction, leading to an improved understanding of reservoir and production systems. Despite the obvious benefits of numerical reservoir models, the development of robust reservoir models is resource-intensive and, thus, can be less attractive (on a cost-ve

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Huang, R., J. A. Pilgrim, P. L. Lewin, D. Scott, and D. Morrice. "Cable tunnel thermal rating prediction using support vector regression." In 2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, 2014. http://dx.doi.org/10.1109/pmaps.2014.6960629.

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Moharana, Bhimasen, Bhramara Bar Biswal, Snehasis Dey, Manas Kumar Rath, and Shobhan Banerjee. "Play Store App Analysis & Rating Prediction Using Classical ML Models & Artificial Neural Network." In 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA). IEEE, 2023. http://dx.doi.org/10.1109/iccubea58933.2023.10391960.

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Wang, Hao, and Martin Ester. "A Sentiment-aligned Topic Model for Product Aspect Rating Prediction." In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/d14-1126.

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Sun, Yongquan, Yingchao Jin, Bo Liu, Quanwu Liu, Chunyu Yu, and Jiahai Zhang. "Classical and Bayes methods for Two-Sample prediction of a Weibull distribution." In 2016 11th International Conference on Reliability, Maintainability and Safety (ICRMS). IEEE, 2016. http://dx.doi.org/10.1109/icrms.2016.8050044.

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Tsai, Bi-Huei, Chih-Huei Chang, George Maroulis, and Theodore E. Simos. "Financial Distress Prediction Using Discrete-time Hazard Model and Rating Transition Matrix Approach." In COMPUTATIONAL METHODS IN SCIENCE AND ENGINEERING: Advances in Computational Science: Lectures presented at the International Conference on Computational Methods in Sciences and Engineering 2008 (ICCMSE 2008). AIP, 2009. http://dx.doi.org/10.1063/1.3225350.

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Reports on the topic "Classical rating prediction methods"

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CHARACTERISATION OF THE BEHAVIOUR OF BEAM-TO-COLUMN STEEL JOINTS UP TO FAILURE. The Hong Kong Institute of Steel Construction, 2022. http://dx.doi.org/10.18057/ijasc.2022.18.3.5.

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The design of steel joints is currently dealt with in Eurocode 3 through the well-known “component method”. In particular, Part 1-8 of this standard provides guidance on how to apply the method to a wide range of joint configurations allowing to assess the latter’s initial rotational stiffness and resistance. Nonetheless, whenever a global structural plastic analysis is contemplated, provisions of Eurocode 3 are insufficient since no clear guidance on how to determine the ultimate resistance and the ultimate rotation capacity of joints is provided. In this paper, the full-range behaviour of be

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