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Cross-Domain Co-Author Recommendation Based on Knowledge Graph Clustering
P. 782–795.
Ahmed Munna M. T., Delhibabu R.
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 address this issue, the endorsement of the cross-disciplinary scientific alliances have been introduced as a new tool for scientific research and technological modernization. In this paper, we use a state-of-art knowledge representation technique named Knowledge Graphs (KGs) and demonstrate how clustering of learned KGs embeddings helps to build a cross-disciplinary co-author recommendation system
Keywords: кластеризациярекомендательные системыclusteringRecommender Systemsknowledge graphsграфы знаний EmbeddingsCross-Domain Researchвекторные представлениямежпредметные исследования
Publication based on the results of:
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Sequential recommender systems have achieved significant success in modeling temporal user behavior but remain limited in cap-turing rich user semantics beyond interaction patterns. Large Language Models (LLMs) present opportunities to enhance user understanding with their reasoning capabilities, yet existing integration approaches cre-ate prohibitive inference costs in real time. To address these limitations, we present a ...
Added: June 18, 2026
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Added: March 25, 2026
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 ...
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One of the main tools for analyzing large volumes of financial data is the use of clustering methods and models, which allow the identification of various patterns. This study examines the problem of clustering time series that reflect the behavior of prices, yields, modes, trends, and a number of related stock indicators. The relevance and ...
Added: February 3, 2026
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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
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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
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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
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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
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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
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The article considers one of the most famous examples of socio-economic systems characterized by significant uncertainty—the S&P-500 stock market, where shares of 500 largest US companies are traded. The flexible algorithm for daily trading has been developed. It is based on known fixed data about cost of shares in previous days as well as on ...
Added: December 19, 2025
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While large transformer models have been successfully used in many real-world applications such as natural language processing, computer vision, and speech processing, scaling transformers for recommender systems remains a challenging problem. Recently, Generative Recommenders framework was proposed to scale beyond typical Deep Learning Recommendation Models (DLRMs). Reformulation of recommendation as sequential transduction task led to ...
Added: November 25, 2025
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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
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Added: November 19, 2025
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Added: November 19, 2025