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Datasets/MovieLens

MovieLens

TabularCustomIntroduced 2016-01-01

The MovieLens datasets, first released in 1998, describe people’s expressed preferences for movies. These preferences take the form of tuples, each the result of a person expressing a preference (a 0-5 star rating) for a movie at a particular time. These preferences were entered by way of the MovieLens web site1 — a recommender system that asks its users to give movie ratings in order to receive personalized movie recommendations.

Source: The MovieLens Datasets: History and Context Image Source: http://files.grouplens.org/papers/harper-tiis2015.pdf

Benchmarks

Click-Through Rate Prediction/AUC Recommendation Systems/HR@10 Recommendation Systems/Recall@10 Recommendation Systems/nDCG@10

Related Benchmarks

MovieLens 100K/Recommendation Systems/RMSE MovieLens 100K/Recommendation Systems/RMSE (Random 90/10 Splits)MovieLens 100K/Recommendation Systems/RMSE (u1 Splits)MovieLens 10M/Recommendation Systems/HR@10 MovieLens 10M/Recommendation Systems/HR@100 MovieLens 10M/Recommendation Systems/MAP@15 MovieLens 10M/Recommendation Systems/MAP@30 MovieLens 10M/Recommendation Systems/MAP@5 MovieLens 10M/Recommendation Systems/NDCG MovieLens 10M/Recommendation Systems/NDCG@15 MovieLens 10M/Recommendation Systems/NDCG@30 MovieLens 10M/Recommendation Systems/NDCG@5 MovieLens 10M/Recommendation Systems/PSP@10 MovieLens 10M/Recommendation Systems/Precision MovieLens 10M/Recommendation Systems/RMSE MovieLens 10M/Recommendation Systems/Recall MovieLens 10M/Recommendation Systems/nDCG@10 MovieLens 10M/Recommendation Systems/nDCG@100 MovieLens 1M/Click-Through Rate Prediction/AUC MovieLens 1M/Click-Through Rate Prediction/Accuracy MovieLens 1M/Click-Through Rate Prediction/Log Loss MovieLens 1M/Collaborative Filtering/NDCG@20 MovieLens 1M/Collaborative Filtering/Recall@20 MovieLens 1M/General Knowledge/NDCG MovieLens 1M/General Knowledge/RMSE MovieLens 1M/Inductive knowledge graph completion/Hits@10 MovieLens 1M/Inductive knowledge graph completion/Mean Rank MovieLens 1M/Knowledge Graph Completion/Hits@10 MovieLens 1M/Knowledge Graph Completion/Mean Rank MovieLens 1M/Knowledge Graphs/Hits@10 MovieLens 1M/Knowledge Graphs/Mean Rank MovieLens 1M/Large Language Model/Hits@10 MovieLens 1M/Large Language Model/Mean Rank MovieLens 1M/Link Prediction/AUC MovieLens 1M/Link Prediction/AUPR MovieLens 1M/Recommendation Systems/HR@10 MovieLens 1M/Recommendation Systems/HR@10 (99 Neg. Samples)MovieLens 1M/Recommendation Systems/HR@10 (full corpus)MovieLens 1M/Recommendation Systems/HR@100 MovieLens 1M/Recommendation Systems/HR@20 MovieLens 1M/Recommendation Systems/HR@5 MovieLens 1M/Recommendation Systems/HR@5 (99 Neg. Samples)MovieLens 1M/Recommendation Systems/HR@50 MovieLens 1M/Recommendation Systems/MRR (99 Neg. Samples)MovieLens 1M/Recommendation Systems/MRR@10 MovieLens 1M/Recommendation Systems/MRR@20 MovieLens 1M/Recommendation Systems/MRR@50 MovieLens 1M/Recommendation Systems/NDCG MovieLens 1M/Recommendation Systems/NDCG@10 MovieLens 1M/Recommendation Systems/NDCG@10 (99 Neg. Samples)MovieLens 1M/Recommendation Systems/NDCG@10 (full corpus)MovieLens 1M/Recommendation Systems/NDCG@20 MovieLens 1M/Recommendation Systems/NDCG@5 MovieLens 1M/Recommendation Systems/NDCG@5 (99 Neg. Samples)MovieLens 1M/Recommendation Systems/NDCG@50 MovieLens 1M/Recommendation Systems/PSP@10 MovieLens 1M/Recommendation Systems/Precision MovieLens 1M/Recommendation Systems/RMSE MovieLens 1M/Recommendation Systems/nDCG@10 MovieLens 1M/Recommendation Systems/nDCG@100 MovieLens 20M/Click-Through Rate Prediction/AUC MovieLens 20M/Recommendation Systems/HR@10 MovieLens 20M/Recommendation Systems/HR@10 (full corpus)MovieLens 20M/Recommendation Systems/Recall@10 MovieLens 20M/Recommendation Systems/Recall@100 MovieLens 20M/Recommendation Systems/Recall@2 MovieLens 20M/Recommendation Systems/Recall@20 MovieLens 20M/Recommendation Systems/Recall@50 MovieLens 20M/Recommendation Systems/nDCG@10 MovieLens 20M/Recommendation Systems/nDCG@10 (full corpus)MovieLens 20M/Recommendation Systems/nDCG@100 MovieLens 25M/Link Prediction/Hits@10 MovieLens 25M/Link Prediction/nDCG@10 MovieLens-Latest/Recommendation Systems/Recall@10 MovieLens-Latest/Recommendation Systems/mAP@10

Statistics

Papers: 1,246
Benchmarks: 4

Links

Tasks

Click-Through Rate Prediction Collaborative Filtering Explainable Recommendation Knowledge Graph Completion Link Prediction Movie Recommendation Multi-Media Recommendation Multibehavior Recommendation Recommendation Systems Recommendation Systems (Item cold-start)Sequential Recommendation