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SplitLight: An Exploratory Toolkit for Recommender Systems Datasets and Splits
P. 3434–3441.
Offline evaluation of recommender systems is often affected by hidden, under-documented choices in data preparation. Seemingly minor decisions in filtering, handling repeats, cold-start treatment, and splitting strategy design can substantially reorder model rankings and undermine reproducibility and cross-paper comparability.
In this paper, we introduce SplitLight, an open-source exploratory toolkit that enables researchers and practitioners designing preprocessing and splitting pipelines or reviewing external artifacts to make these decisions measurable, comparable, and reportable. Given an interaction log and derived split subsets, SplitLight analyzes core and temporal dataset statistics, characterizes repeat consumption patterns and timestamp anomalies, and diagnoses split validity, including temporal leakage, cold-user/item exposure, and distribution shifts. SplitLight further allows side-by-side comparison of alternative splitting strategies through comprehensive aggregated summaries and interactive visualizations. Delivered as both a Python toolkit and an interactive no-code interface, SplitLight produces audit summaries that justify evaluation protocols and support transparent, reliable, and comparable experimentation in recommender systems research and industry. Code is available at https://github.com/monkey0head/SplitLight.
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Association for Computing Machinery (ACM), 2026.
Severin N., Kartushov D., Urzhumov V. et al., , in: Advances in Information Retrieval: 48th European Conference on Information Retrieval, ECIR 2026, Delft, The Netherlands, March 29 – April 2, 2026, Proceedings, Part II. (LNCS, volume 16484).: Cham: Springer Publishing Company, 2026. P. 508–517.
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
Liakhnovich K., Lashinin O., Babkin A. et al., Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval 2025 P. 2754–2758
Relevance and diversity are critical objectives in modern information retrieval (IR), particularly in recommender systems. Achieving a balance between relevance (exploitation) and diversity (exploration) optimizes user satisfaction and business goals such as catalog coverage and novelty. While existing post-processing reranking methods address this trade-off, they usually rely on greedy strategies, leading to suboptimal outcomes for ...
Added: February 3, 2026
Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders
Gusak D., Volodkevich A., Klenitskiy A. et al., , in: RecSys '25: Proceedings of the Nineteenth ACM Conference on Recommender Systems.: ACM, 2025. P. 874–883.
Modern sequential recommender systems, ranging from lightweight transformer-based variants to large language models, have become increasingly prominent in academia and industry due to their strong performance in the next-item prediction task. Yet common evaluation protocols for sequential recommendations remain insufficiently developed: they often fail to reflect the corresponding recommendation task accurately, or are not aligned ...
Added: January 26, 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
Anna Volodkevich, Ivanova V., Vasilev A. et al., , in: Advances in Information Retrieval: 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6–10, 2025, Proceedings, Part IV.: Springer, 2025. P. 425–430.
Simulators for recommender systems are widely used for recommender systems performance evaluation and feedback loop effects analysis. Existing simulators often propose inflexible pipelines, are focused on narrow research tasks, or are not adapted to work with industrial large data volumes. To address these challenges, we developed the Sim4Rec simulation framework. The Sim4Rec models key aspects ...
Added: April 10, 2025
Gleb Mezentsev, Danil Gusak, Ivan Oseledets et al., , in: RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems.: Association for Computing Machinery (ACM), 2024. P. 475–485.
Added: January 16, 2025
M. Shirokikh, Shenbin I., Alekseev A. et al., Journal of Mathematical Sciences 2024 Vol. 285 No. 2 P. 255–284
Over the last several decades, recommender systems have become an integral part of both our daily lives and the research frontier at machine learning. In this survey, we explore various approaches to developing simulators for recommendation systems, especially for modeling the user response function. We consider simple probabilistic models, approaches based on generative adversarial networks, ...
Added: November 24, 2024
Tamm Y., Damdinov R., Vasilev A., , in: RecSys '21: Proceedings of the 15th ACM Conference on Recommender Systems.: Association for Computing Machinery (ACM), 2021. P. 708–713.
Added: November 24, 2024
Association for Computing Machinery (ACM), 2021.
Added: November 24, 2024
Klenitskiy Anton, Alexey Vasilev, , in: RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems.: Association for Computing Machinery (ACM), 2023. P. 1120–1125.
Added: November 24, 2024
Vasilev A., Anna Volodkevich, Kulandin D. et al., , in: RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems.: Association for Computing Machinery (ACM), 2024. P. 1191–1194.
Added: November 24, 2024
Shirokikh M., Shenbin I., Alekseev A. et al., , in: SIGIR '24: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval.: Association for Computing Machinery (ACM), 2024. P. 2553–2558.
Added: November 23, 2024
Anton Klenitskiy, Anna Volodkevich, Pembek A. et al., , in: RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems.: Association for Computing Machinery (ACM), 2024. P. 1067–1072.
Sequential recommender systems are an important and demanded area of research. Such 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: November 7, 2024
Danil Gusak, Mezentsev G., Oseledets I. et al., , in: CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management.: NY: Association for Computing Machinery (ACM), 2024. P. 3772–3776.
Added: September 11, 2024
NY: Association for Computing Machinery (ACM), 2024.
This year, the Short Research Paper track has been very competitive, with very high-quality submissions. Each paper received at least three reviews and was assigned one Senior PC member, who led discussions on the merits and weaknesses of each submission and gave a final recommendation. Based on the reviews, the SPC recommendations, and our own ...
Added: September 10, 2024
Ananyeva M., Lashinin O., Kuznetsova M., , in: Proceedings of the Fourth Knowledge-aware and Conversational Recommender Systems Workshop co-located with 16th ACM Conference on Recommender Systems (RecSys 2022)Vol. 3294.: CEUR Workshop Proceedings, 2022. P. 22–28.
Knowledge-aware recommender systems incorporate side information to improve recommendation performance. The authors of new algorithms are usually focused on developing new ideas behind the proposed methods and comparing their models with existing knowledge-aware recommender models. Meanwhile, some commonly used state-of-the-art general top-n recommender models are ignored as potential baselines. In this study, we compare previously ...
Added: January 5, 2024
Association for Computing Machinery (ACM), 2022.
ACM COPYRIGHT NOTICE. Copyright © 2022 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice ...
Added: January 5, 2024
Association for Computing Machinery (ACM), 2023.
ACM COPYRIGHT NOTICE. Copyright ©2023 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and ...
Added: September 27, 2023