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September 22, 2026
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.
September 21, 2026
Researchers Develop Methodology to Assess the Quality of Legal Representation in Criminal Proceedings
Having a good defence attorney in criminal proceedings can largely determine whether a defendant retains their freedom, health and good name. Researchers at HSE University propose a method for predicting an attorney’s performance based on the outcomes of their previous cases. The methodology takes into account the severity of the charges, the complexity of the cases, and the most likely outcome, drawing on judicial statistics.
September 21, 2026
Algebra, Geometry, and AI: Russian and Vietnamese Mathematicians Discuss Current Research
A delegation of scientists from Hanoi visited the HSE Faculty of Computer Science and then took part in a Russian-Vietnamese conference in St Petersburg. The events were part of the three-year project ‘Flexibility and Computational Methods.’ Over the course of the project, the researchers have prepared joint publications and obtained new mathematical results.

 

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A Method for Audience Extending in Programmatic Advertising by Using Parsimonious Generalization of User Segments

P. 837–841.
Frolov D., Taran Z., Mirkin B.

We propose a novel method for efficient target audience augmentation in programmatic digital advertising. This method utilizes a novel ParGenFS algorithm for most adequate generalization in taxonomies which was developed by the authors in a joint work. The ParGenFS extends user segments by parsimoniously lifting them off-line as a fuzzy set over IAB content taxonomy into a higher rank ‘head subject’. This algorithm was initially intended as an intelligent information retrieval tool. Here it is applied to a very different task of targeted advertisement as an effective tool for augmenting audiences.

Language: English
DOI
Text on another site
Keywords: targeting GeneralizationProgrammatic advertisingAudience extending Taxonomy
Publication based on the results of:
A study of models of decision-making and analysis of complex structured data (2019)

In book

International Conference on Human Interaction and Emerging Technologies
Springer, 2020.
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