{"url":"/dataset/mslr-web10k","name":"MSLR-WEB10K","full_name":null,"description_markdown":"The **MSLR-WEB10K** dataset consists of 10,000 search queries over the documents from search results. The data also contains the values of 136 features and a corresponding user-labeled relevance factor on a scale of one to five with respect to each query-document pair. It is a subset of the MSLR-WEB30K dataset.\r\n\r\nSource: [Dueling Bandits with Qualitative Feedback](https://arxiv.org/abs/1809.05274)","description_withheld":null,"homepage":"https://www.microsoft.com/en-us/research/project/mslr/","introduced_date":"2013-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/13062597","title":"Introducing LETOR 4.0 Datasets","first_author":"Tao Qin","url":null},"license":{"name":"Custom","url":"https://www.microsoft.com/en-us/research/project/mslr/"},"modalities":[{"name":"Ranking","url":"/datasets/modality/ranking"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Information Retrieval","url":"/task/information-retrieval","datasets_with_task":"/datasets/task/information-retrieval"},{"name":"Learning-To-Rank","url":"/task/learning-to-rank","datasets_with_task":"/datasets/task/learning-to-rank"}],"languages":[],"variants":["MSLR-WEB10K"],"data_loaders":[],"num_papers_in_archive":36,"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."}