{"url":"/dataset/top-n-recommendation-runs","name":"Top-N Recommendation Runs","full_name":null,"description_markdown":"We ran 21 recommender systems on three datasets (BeerAdvocate, LibraryThing and MovieLens 1M). The output of these recommenders was evaluated using rec_eval tool. We also measured statistically significant improvements using permutation test. The output of both tools can be found in data.","description_withheld":null,"homepage":"https://github.com/dvalcarce/evalMetrics","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Recommendation Systems","url":"/task/recommendation-systems","datasets_with_task":"/datasets/task/recommendation-systems"}],"languages":[],"variants":["Top-N Recommendation Runs"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}