MovieLens 100k

The 'MovieLens-100k' dataset contains 100,000 ratings from users on various movies and is used to evaluate the performance of recommendation algorithms in understanding user preferences and collaboration dynamics among content creators.

Papers using MovieLens 100k (14)

Vectorized Context-Aware Embeddings for GAT-Based Collaborative Filtering2025 · 1 citesRSAttAE: An Information-Aware Attention-based Autoencoder Recommender System2025 · 1 citesCounterfactual Risk Minimization with IPS-Weighted BPR and Self-Normalized Evaluation in Recommender Systems2025End-to-End Personalization: Unifying Recommender Systems with Large Language Models2025Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendations2025Counterfactual Explanations for Neural Recommenders2021 · 77 citesZero-Shot Next-Item Recommendation using Large Pretrained Language Models2023 · 23 citesOne-at-a-time: A Meta-Learning Recommender-System for Recommendation-Algorithm Selection on Micro Level2018 · 9 citesMovie Recommendation with Poster Attention via Multi-modal Transformer Feature Fusion2024 · 1 citesMulti-Linear Interactive Matrix Factorization2014A Theoretical Analysis of Two-Stage Recommendation for Cold-Start Collaborative Filtering2016Bayesian inference for bivariate ranks2018User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation2019Uptrendz: API-Centric Real-time Recommendations in Multi-Domain Settings2023