arXiv Machine Learning June 8, 2026 By Ekaterina Grishina, Stepan Kuznetsov, Askar Tsyganov, Ilya Ivanov, Daria Korovaitceva, Margarita Rusanova, Uliana Parkina, Alexander Derevyagin, Evgeny Frolov, Sergey Samsonov, Anton Lysenko

Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies

Read the original on arXiv Machine Learning →

arXiv:2606. 07492v1 Announce Type: cross Abstract: The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale.

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arXiv Machine Learning

Jun 29

Benchmarking on Tasks That Matter: Dataset Selection for Preserving Model Rankings

arXiv:2606. 27997v1 Announce Type: new Abstract: Benchmarks of machine learning models often include many datasets, making evaluation expensive.

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arXiv Machine Learning

Jun 9

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arXiv Machine Learning

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A Judge-Aware Ranking Framework for Evaluating Large Language Models without Ground Truth

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arXiv Machine Learning

Jun 9

Generalized Rank-based Evaluation for Knowledge Graph Completion: Perspectives, Framework, and Analyses

arXiv:2606. 08921v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to predict missing facts from an observed knowledge graph (KG), playing a crucial role in a wide range of real-world applications such as drug discovery, recommender systems, and retrieval-augmented generation (RAG).

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arXiv AI

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LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking

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Obliviate: Efficient Unlearning in Recommender Systems

arXiv:2607. 22665v1 Announce Type: new Abstract: Machine unlearning is becoming increasingly critical in the context of data privacy regulations, particularly for recommendation systems that are directly trained on user interaction data.

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