MIND

The MIND dataset is a benchmark that contains user interactions with news articles and is used to evaluate the performance of recommender systems, particularly in the context of news recommendation.

Papers using MIND (15)

Enhancing News Recommendation with Hierarchical LLM Prompting2025 · 3 citesAMEM4Rec: Leveraging Cross-User Similarity for Memory Evolution in Agentic LLM Recommenders2026Addressing Cold Start For next-article Recommendation2025Democratizing News Recommenders: Modeling Multiple Perspectives for News Candidate Generation with VQ-VAE2025Revisiting Language Models in Neural News Recommender Systems2025Invariant debiasing learning for recommendation via biased imputation2024 · 5 citesPrompt Learning for News Recommendation2023 · 95 citesCoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation2024 · 9 citesMIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation2024 · 8 citesUser recommendation system based on MIND dataset2022 · 3 citesGraph-Based Model-Agnostic Data Subsampling for Recommendation Systems2023 · 3 citesLearning to Select Historical News Articles for Interaction based Neural News Recommendation2021 · 2 citesEfficient Pointwise-Pairwise Learning-to-Rank for News Recommendation2024 · 2 citesAspect-driven User Preference and News Representation Learning for News Recommendation2021Rethinking negative sampling in content-based news recommendation2024