Netflix
Emerging24papers using it
2012first seen
The 'Netflix' dataset is a benchmark containing user ratings for movies, used to evaluate the performance of recommendation systems, particularly in terms of prediction accuracy and diversity.
Papers using Netflix (24)
- LLM Reasoning for Cold-Start Item RecommendationUncertainty-Aware Semantic Decoding for LLM-Based Sequential RecommendationHI-Series Algorithms A Hybrid of Substance Diffusion Algorithm and Collaborative FilteringLatent Relational Metric Learning via Memory-based Attention for Collaborative RankingModeling Dynamic User Preference via Dictionary Learning for Sequential RecommendationExplanations for Temporal RecommendationsCollaborative Filtering with User-Item Co-Autoregressive ModelsLLMRec: Large Language Models with Graph Augmentation for RecommendationEnhancing VAEs for Collaborative Filtering: Flexible Priors & Gating MechanismsA new system-wide diversity measure for recommendations with efficient algorithmsCold-start Problems in Recommendation Systems via Contextual-bandit AlgorithmsWasserstein Autoencoders for Collaborative FilteringImproving Recommendation Diversity by Highlighting the ExTrA Fabricated ExpertsDynamic Collaborative Filtering with Compound Poisson FactorizationA two-step Recommendation Algorithm via Iterative Local Least SquaresInformation Filtering via Balanced Diffusion on Bipartite NetworksConsistence beats causality in recommender systemsRecFusion: A Binomial Diffusion Process for 1D Data for RecommendationBudget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal DesignMTS Kion Implicit Contextualised Sequential Dataset for Movie RecommendationDebiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender SystemsUltra accurate collaborative information filtering via directed user similarityA vertex similarity index for better personalized recommendationDiffusion-like recommendation with enhanced similarity of objects