?
Collaborator Recommender System
P. 101–119.
Anna Averchenkova, Alina Akhmetzyanova, Sudarikov K., Stanislav Petrov, Makarov I., Pendiukhov M., Zhukov L. E.
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, we propose a recommender system for the scientists from the Higher School of Economics university to help them find co-authors for their prospective studies.
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., User Modeling and User-Adapted Interaction 2025 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: March 14, 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
Klenitskiy A., Anna Volodkevich, Pembek A. et al., ACM Transactions on Recommender Systems 2026
Sequential recommender systems are an important and in-demand area of research. These systems aim to use the order of interactions in a user’s history to predict future interactions. The premise is that the order of interactions and sequential patterns play an essential role. Therefore, it is crucial to use datasets that exhibit a sequential structure ...
Added: January 28, 2026
Anna Volodkevich, Danil Gusak, Klenitskiy A. et al., User Modelling and User-Adapted Interaction 2025 No. 35 Article 13
The goal of modern sequential recommender systems is often formulated in terms of next-item prediction. In this paper, we explore the applicability of transformer-based generative models for the Top-K sequential recommendation task, where the goal is to predict items that a user is likely to interact with in the “near future.” This goal aligns with ...
Added: January 26, 2026
Klenitskiy A., Fatkulin A., Denisova D. et al., , in: RecSysChallenge '25: Proceedings of the Recommender Systems Challenge 2025.: Association for Computing Machinery (ACM), 2025. P. 26–30.
Building universal user representations that capture the essential aspects of user behavior is a crucial task for modern machine learning systems. In real-world applications, a user’s historical interactions often serve as the foundation for solving a wide range of predictive tasks, such as churn prediction, recommendations, or lifetime value estimation. Using a task-independent user representation ...
Added: January 26, 2026
Ivanova V., Frolov E., Vasilev A., , in: RecSys '25: Proceedings of the Nineteenth ACM Conference on Recommender Systems.: ACM, 2025. P. 1142–1147.
We consider the task of learning from both positive and negative feedback in a sequential recommendation scenario, as both types of feedback are often present in user interactions. Meanwhile, conventional sequential learning models usually focus on considering and predicting positive interactions, ignoring that reducing items with negative feedback in recommendations improves user satisfaction with the ...
Added: January 26, 2026
Pembek A., Fatkulin A., Klenitskiy A. et al., , in: RecSys '25: Proceedings of the Nineteenth ACM Conference on Recommender Systems.: ACM, 2025. P. 626–631.
Many sequential recommender systems suffer from the cold start problem, where items with few or no interactions cannot be effectively used by the model due to the absence of a trained embedding. Content-based approaches, which leverage item metadata, are commonly used in such scenarios. One possible way is to use embeddings derived from content features ...
Added: January 26, 2026
Association for Computing Machinery (ACM), 2026.
KDD is the premier Data Science and AI conference, hosting both a Research and an Applied Data Science Track. The conference will take place from August 9 to 13, 2026, in Jeju, Korea. ...
Added: November 25, 2025
Makeev S., Andreev A., Baikalov V. et al., , in: RecSysChallenge '25: Proceedings of the Recommender Systems Challenge 2025.: Association for Computing Machinery (ACM), 2025. P. 21–25.
This paper describes the 4th-place solution by team ambitious for the RecSys Challenge 2025, organized by Synerise and ACM RecSys, which focused on universal behavioral modeling. The challenge objective was to generate user embeddings effective across six diverse downstream tasks. Our solution integrates (1) a sequential encoder to capture the temporal evolution of user interests, (2) a ...
Added: November 19, 2025
Association for Computing Machinery (ACM), 2025.
Added: November 19, 2025
Khrylchenko K., Baikalov V., Makeev S. et al., , in: RecSys '25: Proceedings of the Nineteenth ACM Conference on Recommender Systems.: ACM, 2025. P. 545–550.
Two-tower neural networks are a popular architecture for the retrieval stage in recommender systems. These models are typically trained with a softmax loss over the item catalog. However, in web-scale settings, the item catalog is often prohibitively large, making full softmax infeasible. A common solution is sampled softmax, which approximates the full softmax using a ...
Added: November 19, 2025
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
Nikita Severin, Savchenko A., Kiselev D. et al., , in: RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems.: Association for Computing Machinery (ACM), 2023.
Recommender systems are essential for personalized content delivery and have become increasingly popular recently. However, traditional recommender systems are limited in their ability to capture complex relationships between users and items. Dynamic graph neural networks (DGNNs) have recently emerged as a promising solution for improving recommender systems by incorporating temporal and sequential information in dynamic ...
Added: May 22, 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
Shevchenko V., Belousov N., Vasilev A. et al., , in: KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.: Association for Computing Machinery (ACM), 2024. P. 5701–5712.
In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets. However, this approach may fail to holistically reflect their effectiveness due to the significant impact of dataset characteristics on algorithm performance. Addressing this deficiency, this paper introduces a novel ...
Added: November 24, 2024
Evgeny Frolov, Tatyana Matveeva, Мирвахабова Л. et al., , in: RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems.: Association for Computing Machinery (ACM), 2024. P. 981–986.
Added: September 10, 2024
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