MIND
Emerging17papers using it
2021first seen
The MIND dataset is a benchmark that contains user interaction data for news articles and is used to evaluate the performance of recommender systems, particularly in the context of collaborative filtering.
Papers using MIND (17)
- Enhancing News Recommendation with Hierarchical LLM PromptingRevisiting Language Models in Neural News Recommender SystemsAMEM4Rec: Leveraging Cross-User Similarity for Memory Evolution in Agentic LLM RecommendersModeling Behavioral Patterns in News Recommendations Using Fuzzy Neural NetworksAddressing Cold Start For next-article RecommendationDemocratizing News Recommenders: Modeling Multiple Perspectives for News Candidate Generation with VQ-VAECoST: Contrastive Quantization based Semantic Tokenization for Generative RecommendationMIND Your Language: A Multilingual Dataset for Cross-lingual News RecommendationUser recommendation system based on MIND datasetGraph-Based Model-Agnostic Data Subsampling for Recommendation SystemsLearning to Select Historical News Articles for Interaction based Neural News RecommendationEfficient Pointwise-Pairwise Learning-to-Rank for News RecommendationNews Recommendation with Category Description by a Large Language ModelAspect-driven User Preference and News Representation Learning for News RecommendationUnderstanding the Relation of User and News Representations in Content-Based Neural News RecommendationTopic-Centric Explanations for News RecommendationRethinking negative sampling in content-based news recommendation