{"url":"/dataset/w3c-experts","name":"W3C Experts","full_name":null,"description_markdown":"This is a subset of the TREC 2005 enterprise track data, and consists of 48 topics and 200 candidates per topic, with each candidate labeled as an expert or non-expert for the topic. The task is to rank the candidates based on their expertise on a topic, using a corpus of mailing lists from the World Wide Web Consortium (W3C). This is an application where the  unconstrained algorithm does better for the minority protected group.","description_withheld":null,"homepage":"https://github.com/MilkaLichtblau/DELTR-Experiments/tree/master/data/TREC","introduced_date":"2018-05-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/reducing-disparate-exposure-in-ranking-a","title":"Reducing Disparate Exposure in Ranking: A Learning To Rank Approach","first_author":null,"url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["W3C Experts"],"data_loaders":[],"num_papers_in_archive":4,"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."}