Researchers Rank Recommendation Algorithms Using Sports Tournament Model
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed an approach for selecting recommendation algorithms more effectively. Their approach uses pairwise comparisons of algorithms to create a tournament table, with the overall ranking based on their performance across all datasets in the tournament. This can reduce the number of algorithms that need to be tested when developing new services, saving both time and money. The study was presented at the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026).
Recommendation systems determine which products, films, songs, or publications to show users. To do this, they analyse users’ past behaviour and predict what they might be interested in next. For example, recommendation systems identify users with similar interests and take into account the sequence of their views or purchases.
However, there is no universal recommendation algorithm. A method that works well for an online store may not be suitable for an online cinema. Therefore, algorithms are usually pre-tested on existing datasets, and their performance is averaged across them. This approach, however, has its limitations: the resulting ranking does not account for the specifics of individual datasets and may be unstable, while testing all algorithms online with real users is costly and risky.
Researchers at HSE University have developed a methodology for comparing recommendation algorithms based on the Bradley–Terry model, which is used to rank competitors based on the outcomes of pairwise ‘matches.’ In this study, the recommendation algorithms were the ‘players,’ while the ‘matches’ were tests of the algorithms on different datasets. Two algorithms were compared using a selected metric, such as recommendation accuracy, and the one with the higher score was declared the winner. Based on the results of all pairwise comparisons, the model estimated the relative strength of each algorithm. The researchers trained and tested 14 algorithms on 89 datasets from different fields.
The results showed that the same algorithms could occupy different positions in the ranking depending on the type of data used in the ‘matches.’ For example, on sequential datasets, where the order of user actions is important, SASRec and GASATF ranked as the top performers. However, when a dataset did not contain an explicit sequence, these algorithms dropped to tenth and eleventh place, respectively, while LightGCN and ALS topped the ranking.
Additionally, the researchers tested the robustness of the rankings to incomplete data. The ranking produced by the Bradley–Terry model remained stable even when some comparisons were missing, meaning that not every algorithm was compared with every other algorithm. The authors also tested an extended version of the model on additional datasets, taking the context into account. In this case, the model had to predict the winners without directly comparing the recommendation algorithms.
'The idea of using a sports model came to us thanks to the work of our senior colleague Vladimir Spokoiny. To draw an analogy with a sports tournament, the outcome of a match is influenced not only by the players themselves but also by conditions such as the city or the weather. In our study, the characteristics of the dataset served as this context, including the number of users and items, the average length of users’ histories, and other parameters. If we train the model to take this context into account alongside the results of previous comparisons, it can use the characteristics of a new dataset to predict in advance which algorithms are likely to perform best,' says co-author Anton Lysenko, Expert at the International Laboratory of Stochastic Algorithms and High-Dimensional Inference.
In 78% of cases, the algorithm ranked first by the model was indeed among the top three. With the conventional approach of averaging performance metrics, this was the case in only 16% of cases. The authors attribute this difference to the fact that, unlike heuristic comparison methods, their model has a sound theoretical foundation, making the resulting rankings more reliable.
'Our approach helps identify which algorithms are best suited to a particular dataset before they are tested online. For example, if a bank needs to recommend loyalty programmes, it can feed the characteristics of a new dataset into the model to identify the most promising algorithms. Only these algorithms would then need to be tested on real users, rather than all available options. This saves both time and resources,' says co-author Sergey Samsonov, Head of the International Laboratory of Stochastic Algorithms and High-Dimensional Inference.
The study was carried out as part of a programme implemented by the HSE AI Research Centre and supported by a grant from the Ministry of Economic Development of the Russian Federation.
See also:
HSE Researchers Create New Corpus of Early Child Speech in Russian
Researchers at the HSE Centre for Language and Brain have presented RusLan-M, an open multimedia corpus that makes it possible to trace the development of early child speech in Russian from first words to the emergence of complex grammatical constructions. The database contains around 41 hours of video recordings and more than 35,000 child utterances. The new resource will help researchers study more precisely how children acquire Russian and, in the longer term, develop more reliable tools for assessing speech development. The study has been published in Language Resources and Evaluation.
Hybrid Intelligence: Competencies in the Age of AI Discussed at Technoprom-2026
Artificial intelligence is not creating new professions, but rather transforming the nature of existing ones. This was the conclusion reached by participants in the panel session ‘Hybrid Intelligence: Digital and Human Drivers of Development,’ organised by the Institute for Statistical Studies and Economics of Knowledge (ISSEK) at HSE University as part of the 13th International Forum of Technological Development (Technoprom-2026). The experts discussed how the nature of work is changing, which skills are becoming increasingly sought after, and what prevents companies from fully capitalising on new technologies.
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
Researchers at HSE University and Sber Train Neural Networks to Better Predict User Preferences
The HSE FCS AI and Digital Science Institute and Sber have introduced a new architecture for recommendation systems that combines two classes of models, enabling algorithms to better predict users’ interests and needs. A preprint of the paper has been published on arxiv.org and presented at Urban ML.
Physicists Discover What Happens Inside a Stable Vortex
Large vortices with characteristic spiral arms are often observed in the atmosphere and the ocean. Physicists from HSE University have explained how these structures form and why they retain their shape. The researchers found that velocities at points located along the same vortex arc remain correlated even over long distances. At the same time, this correlation weakens rapidly with increasing distance from the vortex centre. These differences help explain the formation of spiral arms and may improve models of atmospheric and oceanic currents. The findings have been published in Physical Review Fluids.
‘Working with AI Solves a Wide Range of Engineering Problems’
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
‘The Peak of Stupidity’ and ‘The Valley of Despair’: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.
Toffee and Risk: Scientists Discover Why People Who Crave Sweets Make More Impulsive Choices
Having a sweet tooth may be linked not only to eating habits but also to the way people make decisions. Researchers at HSE University have found that people with a preference for sweet foods tend to behave more impulsively—not because they want immediate rewards, but because they are less willing to tolerate uncertainty. These findings may help improve treatments for addiction. The study findings have been published in Frontiers in Psychology.
Physicists Find a Way to Model Ion Parameters in Plasma in Seconds
Researchers from HSE University and the Moscow Institute of Physics and Technology (MIPT) have developed a set of simple analytical methods for calculating the properties of heavy ions in helium under the influence of a strong electric field. The new approach speeds up calculations of ion mobility and ion–molecule reaction rates by thousands of times while maintaining sufficient accuracy for plasma jet modelling. The findings have been published in the journal Physica Scripta.