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Метод скользящего контроля для оценки качества рекомендательных интернет-сервисов
С. 175–182.
Yusupov V., Sukhorukov N., Frolov E., User Modelling and User-Adapted Interaction 2026 Vol. 36 Article 2
Graph-based recommender systems have emerged as a powerful paradigm for personalized recommendations. However, their reliance on full model retraining to incorporate new users or new interactions creates scalability barriers. The task becomes infeasible in real-life recommender systems due to excessive time and resource costs involved. To address this limitation, we propose a fast and efficient ...
Added: March 15, 2026
Yusupov V., Sukhorukov N., Frolov E., , in: User Modeling and User-Adapted Interaction.: Springer, 2026. Ch. 36.2 P. 1–24.
Graph-based recommender systems have emerged as a powerful paradigm for personalized recommendations. However, their reliance on full model retraining to incorporate new users or new interactions creates scalability barriers. The task becomes infeasible in real-life recommender systems due to excessive time and resource costs involved. To address this limitation, we propose a fast and efficient ...
Added: January 29, 2026
Yusupov V., Rakhuba M., Frolov E., , in: CIKM '25: Proceedings of the 34rd ACM International Conference on Information and Knowledge Management.: ACM, 2025. Ch. 1 P. 5469–5473.
In this work, we present a fast and effective Linear approach for updating recommendations in a scalable graph-based recommender system UltraGCN. Solving this task is extremely important to maintain the relevance of the recommendations under the conditions of a large amount of new data and changing user preferences. To address this issue, we adapt the ...
Added: October 3, 2025
Yusupov V., Rakhuba M., Frolov E., , in: RecSys '25: Proceedings of the Nineteenth ACM Conference on Recommender Systems.: ACM, 2025. Ch. 1 P. 1217–1221.
Recent studies have demonstrated the potential of hyperbolic geometry for capturing complex patterns from interaction data in recommender systems. In this work, we introduce a novel hyperbolic recommendation model that uses geometrical insights to improve representation learning and increase computational stability at the same time. We reformulate the notion of hyperbolic distances to unlock additional ...
Added: October 3, 2025
I. Safilo, D. Tikhonovich, Petrov A. et al., Doklady Mathematics 2023 Vol. 108 No. 2 P. S456–S464
We present a new movie and TV show recommendation dataset collected from the real users of MTS Kion video-on-demand platform. In contrast to other popular movie recommendation datasets, such as MovieLens or Netflix, our dataset is based on the implicit interactions registered at the watching time, rather than on explicit ratings. We also provide rich ...
Added: May 24, 2025
Avdasheva S. B., Khomik O., Chesnokov V. et al., Проблемы прогнозирования 2025 № 3 С. 135–145
Over the past quarter-century, digital platforms proliferated and became the world’s
most valuable companies. Traditionally, the growth of digital platforms is explained by crossplatform network effects, which, in turn, are supported by recommendation systems – a set of
algorithms that suggest the most suitable user of one type to a user of another type. The dependence of ...
Added: March 10, 2025
И. Сафило, Тихонович Д., Петров А. et al., Доклады Российской академии наук. Математика, информатика, процессы управления (ранее - Доклады Академии Наук. Математика) 2023 Т. 514 № 2 С. 333–342
We present a new movie and TV show recommendation dataset collected from the real users of MTS Kion video-on-demand platform. In contrast to other popular movie recommendation datasets, such as MovieLens or Netflix, our dataset is based on the implicit interactions registered at the watching time, rather than on explicit ratings. We also provide rich ...
Added: February 13, 2024
Другова Е. А., Журавлева И. И., Zakharova U. et al., Вопросы образования 2022 № 4 С. 107–153
Методы искусственного интеллекта все чаще используются в исследованиях и разработках в области учебной аналитики, призванной анализировать данные, накопленные в процессе обучения, с целью повышения его результативности. С этой же целью развиваются модели педагогического проектирования, самой широко применяемой из которых является ADDIE, раскладывающая создание курса на этапы. Первые две области критикуются за слабую связь с практикой ...
Added: December 1, 2022
Gorbunov I. V., Зайцев А. А., Мещеряков Р. В. et al., Медицинская техника 2016 № 6 С. 24–27
Описано построение рекомендательной системы выбора одного из пяти реабилитационных комплексов немедикаментозной реабилитации участников вооруженных конфликтов и чрезвычайных ситуаций. Разработанные в Томском НИИ курортологии и физиотерапии ФМБА России реабилитационные технологии позволят предупредить возможную хронизацию патологических процессов, повысить адаптационные резервы организма и улучшить качество жизни лиц, пострадавших в чрезвычай-ных ситуациях. Для каждого комплекса сформирован набор признаков, позволяющий ...
Added: September 27, 2021
Ahmed Munna M. T., Delhibabu R., , in: Intelligent Information and Database Systems: 13th Asian Conference, ACIIDS 2021, Phuket, Thailand, April 7–10, 2021, Proceedings.: Springer, 2021. P. 782–795.
Nowadays, due to the growing demand for interdisciplinary research and innovation, different scientific communities pay substantial attention to cross-domain collaboration. However, having only information retrieval technologies in hands might be not enough to find prospective collaborators due to the large volume of stored bibliographic records in scholarly databases and unawareness about emerging cross-disciplinary trends. To ...
Added: January 14, 2021
Сендерович М. А., В кн.: Межвузовская научно-техническая конференция студентов, аспирантов и молодых специалистов им. Е.В. Арменского.: М.: МИЭМ НИУ ВШЭ, 2019. С. 223–224.
Данная работа посвящена актуальной теме автоматизации в машинном обучении на примере создания универсальной рекомендательной системы. В работе исследуются различные типы рекомендательных систем, акцент делается на подходы коллаборативной фильтрации. Изучаются методы автоматизации машинного обучения, на основе которых будет разработана данная рекомендательная система. ...
Added: October 31, 2020
Anna Averchenkova, Alina Akhmetzyanova, Sudarikov K. et al., , in: Network Algorithms, Data Mining, and Applications. Springer Proceedings in Mathematics & Statistics.: Springer, 2020. P. 101–119.
Nowadays, a lot of scientists’ works aim to improve the quality of people’s life but it could be quite complicated without building a successful collaboration. Productive partnerships can increase research efficiency in many cases and make a huge impact on society. However, today there is no clear way to find such collaborators. In this paper, ...
Added: February 27, 2020
Demochkin K., Savchenko A., , in: Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Lecture Notes in Computer Science, Revised Selected PapersVol. 11832.: Cham: Springer, 2019. Ch. 26 P. 291–297.
In this paper we focus on the problem of multi-label image recognition for visually-aware recommender systems. We propose a two stage approach in which a deep convolutional neural network is firstly fine-tuned on a part of the training set. Secondly, an attention-based aggregation network is trained to compute the weighted average of visual features in ...
Added: December 22, 2019
Demochkin K. V., Savchenko A., Journal of Physics: Conference Series 2019 Vol. 1368 No. 032016 P. 1–7
In this paper we focus on the problem of user interests’ classification in visual product recommender systems. We propose the two-stage procedure. At first, the visual features are learned by fine-tuning the convolutional neural network, e.g., MobileNet. At the second stage, we use such learnable pooling techniques as neural aggregation network and context gating in ...
Added: November 29, 2019
Savchenko A., Дёмочкин К. В., Savchenko L., Optical Memory and Neural Networks (Information Optics) 2020 Vol. 29 No. 4 P. 297–304
In this paper, we analyze effective methods of multi-label classification of image sets in development of visual recommender systems. We propose a two-step algorithm, which at the first step performs fine-tuning of a convolutional neural network for extraction of visual features. At the second stage, the algorithm concatenates the obtained feature vectors of each image ...
Added: October 25, 2019
Барт Т. В., Власов В. В., Образовательные ресурсы и технологии 2019 Т. 2 № 27 С. 7–14
Training model information recommendation system is associated with the study of applied mathematical and
information methods and models, their combinations in order to ensure the necessary accuracy of the forecasts
and conclusions. The article deals machine learning of model recommendation system using statistical methods
and analysis of big data, aimed at addressing the issues of individualization of education. ...
Added: September 30, 2019
Gerasimova O., Makarov I., , in: Advances in Computational Intelligence. IWANN 2019.: Berlin: Springer, 2019. P. 667–677.
In this paper, we study the problem of predicting quantity of collaborations in co-authorship network. We formulated our task in terms of link prediction problem on weighted co-authorship network, formed by authors writing papers in co-authorship represented by edges between authors in the network. Our task is formulated as regression for edge weights, for which ...
Added: July 29, 2019
Volkova L. L., Токарева М. М., Ланко А. А., В кн.: Новые информационные технологии в автоматизированных системах. Материалы двадцатого научно-практического семинара.: Институт прикладной математики им. М.В. Келдыша РАН, 2017. С. 239–244.
Данная работа посвящена разработке рекомендательной системы, принимающей в расчёт предпочтения пользователя в книгах: параметрические, жанровые, основанные на прочитанных произведениях. Проведён обзор применения стратегий рекомендательных систем в данной задаче. Описаны ключевые положения для создания рекомендательной системы, предлагающей читателю книги на основе введённых им предпочтений, а также ключевые методы, которые следует положить в основу подсистему извлечения данных ...
Added: January 31, 2018
Karpov N., Shashkin P, , in: WI '17 Proceedings of the International Conference on Web Intelligence.: ACM, 2017. P. 1069–1071.
Improving user experience through personalized recommendations is crucial to organizing the abundance of data on news websites. Modeling user preferences based on implicit feedback has recently gained lots of attention, partly due to growing volume of web generated click stream data. Matrix factorization learned with stochastic gradient descent has successfully been adopted to approximate various ...
Added: November 14, 2017
Makarov I., Bulanov O., Zhukov L. E., , in: Models, Algorithms, and Technologies for Network Analysis. Springer Proceedings in Mathematics & StatisticsVol. 197.: Springer, 2017. P. 251–257.
Modern bibliographic databases contain significant amount of information on publication activities of research communities. Researchers regularly encounter challenging task of selecting a co-author for joint research publication or searching for authors, whose papers are worth reading. We propose a new recommender system for finding possible collaborator with respect to research interests. The recommendation problem is ...
Added: March 3, 2017
Ignatov D. I., Nikolenko S. I., Abaev T. et al., Expert Systems with Applications 2016 Vol. 55 P. 546–558
We present a new recommender system developed for the Russian interactive radio network FMhost. To the best of our knowledge, it is the first model and associated case study for recommending radio stations hosted by real DJs rather than automatically built streamed playlists. To address such problems as cold start, gray sheep, boosting of rankings, preference ...
Added: June 28, 2016
Токарева М. М., Volkova L. L., Абдуллаев А. П., В кн.: Новые информационные технологии в автоматизированных системах: материалы девятнадцатого научно-практического семинара.: М.: Институт прикладной математики им. М.В. Келдыша РАН, 2016. С. 75–79.
Данная работа посвящена рекомендатель ной системе для построения туристических
маршрутов по достопримечательностям и
заведениям ресторанной категори. Пред ложен метод построения маршрута, осно ванный на анализе предпочтений пользова теля, интерпретации данных о культурно досуговых учреждениях и поиске объектов
заданных категорий с применением мура вьиного алгоритма. ...
Added: April 28, 2016
Ignatov D. I., Корнилов Д. И., , in: Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI at IJCAI 2015).: Buenos Aires: [б.и.], 2015. P. 87–98.
We propose a new algorithm for recommender systems with numeric ratings which is based on Pattern Structures (RAPS). As the input the algorithm takes rating matrix, e.g., such that it contains movies rated by users. For a target user, the algorithm returns a rated list of items (movies) based on its previous ratings and ratings ...
Added: October 23, 2015