{"url":"/dataset/kdd12","name":"KDD12","full_name":null,"description_markdown":"A clickthrough prediction dataset, for more information please see the [Kaggle page](https://www.kaggle.com/c/kddcup2012-track2)","description_withheld":null,"homepage":"https://www.kaggle.com/c/kddcup2012-track2","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Click-Through Rate Prediction","url":"/task/click-through-rate-prediction","datasets_with_task":"/datasets/task/click-through-rate-prediction"}],"languages":[{"name":"Russian","url":"/datasets/language/russian"}],"variants":["KDD12"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/click-through-rate-prediction-on-kdd12","task":"Click-Through Rate Prediction","dataset_variant":"KDD12","rows":5,"metrics":["AUC","Log Loss"],"first_row_in_archive_order":{"model":"FCN","paper":"/paper/dcnv3-towards-next-generation-deep-cross","metrics":{"AUC":"0.8098","Log Loss":"0.1494"},"code_links":[{"title":"reczoo/FuxiCTR","url":"https://github.com/reczoo/FuxiCTR"},{"title":"salmon1802/DCNv3","url":"https://github.com/salmon1802/DCNv3"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/dcnv3-towards-next-generation-deep-cross","title":"FCN: Fusing Exponential and Linear Cross Network for Click-Through Rate Prediction","date":"2024-07-18","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/optimizing-feature-set-for-click-through-rate","title":"Optimizing Feature Set for Click-Through Rate Prediction","date":"2023-01-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":1,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/memonet-memorizing-representations-of-all","title":"MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction","date":"2022-10-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/optembed-learning-optimal-embedding-table-for","title":"OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction","date":"2022-08-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/autoint-automatic-feature-interaction","title":"AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks","date":"2018-10-29","rows_on_this_dataset":1,"code_links":19,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":9,"samples_ran":1,"samples_unverified":8,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":2,"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."}