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FCA-Based Recommender Models and Data Analysis for Crowdsourcing Platform Witology
P. 287–292.
This paper considers a recommender part of the data anal- ysis system for the collaborative platform Witology. It was developed by the joint research team of the National Research University Higher School of Economics and the Witology company. This recommender sys- tem is able to recommend ideas, like-minded users and antagonists at the respective phases of a crowdsourcing project. All the recommender meth- ods were tested in the experiments with real datasets of the Witology company.
In book
Vol. 8577: Graph-Based Representation and Reasoning. , Springer, 2014.
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
Ignatov D. I., , in: 11th International Conference, AIST 2023, Yerevan, Armenia, September 28–30, 2023, Revised Selected Papers. Analysis of Images, Social Networks and Texts. Lecture Notes in Computer Science (LNCS, volume 14486).: Cham: Springer, 2024. P. 349 – 361.
This paper dates back to the asymptotic solutions of Rota’s problem on the size of maximum antichain in the set partition lattice by Canfield and Harper and others. The knowledge of asymptotic coefficients could pave the way to the asymptotic solutions of such problems as (maximal) antichain counting in partition lattices. In addition to our ...
Added: January 23, 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
Sorokin P. S., Afanaseva I., Мониторинг общественного мнения: Экономические и социальные перемены 2025 № 4 С. 202–224
The article is devoted to the study of manifestations and methods of supporting agentic (i.e. transforming the environment in a direction not determined by it) behavior as a factor of success of contemporary corporations in the condition of neo-structuration, that is, a new phase of societal evolution, which assumes a change in the relationship between ...
Added: September 5, 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