{"url":"/dataset/ipinyou","name":"iPinYou","full_name":"iPinYou Global RTB Bidding Algorithm Competition Dataset","description_markdown":"The **iPinYou** Global RTB(Real-Time Bidding) Bidding Algorithm Competition is organized by iPinYou from April 1st, 2013 to December 31st, 2013.The competition has been divided into three seasons. For each season, a training dataset is released to the competition participants, the testing dataset is reserved by iPinYou. The complete testing dataset is randomly divided into two parts: one part is the leaderboard testing dataset to score and rank the participating teams on the leaderboard, and the other part is reserved for the final offline evaluation. The participant's last offline submission is evaluated by the reserved testing dataset to get a team's offline final score. This dataset contains all three seasons training datasets and leaderboard testing datasets.The reserved testing datasets are withheld by iPinYou. The training dataset includes a set of processed iPinYou DSP bidding, impression, click, and conversion logs.\n\nSource: [iPinYou Global RTB Bidding Algorithm Competition Dataset](https://contest.ipinyou.com/)\nImage Source: [http://contest.ipinyou.com/ipinyou-dataset.pdf](http://contest.ipinyou.com/ipinyou-dataset.pdf)","description_withheld":null,"homepage":"https://contest.ipinyou.com/","introduced_date":"2014-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"iPinYou Global RTB Bidding Algorithm Competition Dataset","first_author":null,"url":"https://doi.org/10.1145/2648584.2648590"},"license":null,"modalities":[],"tasks":[{"name":"Click-Through Rate Prediction","url":"/task/click-through-rate-prediction","datasets_with_task":"/datasets/task/click-through-rate-prediction"}],"languages":[],"variants":["iPinYou"],"data_loaders":[{"repo":"https://github.com/tensorflow/recommenders","url":"https://github.com/tensorflow/recommenders/tree/v0.5.1","frameworks":["tf"]}],"num_papers_in_archive":22,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/click-through-rate-prediction-on-ipinyou","task":"Click-Through Rate Prediction","dataset_variant":"iPinYou","rows":7,"metrics":["AUC","LogLoss"],"first_row_in_archive_order":{"model":"OPNN","paper":"/paper/product-based-neural-networks-for-user","metrics":{"AUC":"0.8174"},"code_links":[{"title":"shenweichen/DeepCTR","url":"https://github.com/shenweichen/DeepCTR"},{"title":"shenweichen/DeepCTR-Torch","url":"https://github.com/shenweichen/DeepCTR-Torch"},{"title":"xue-pai/FuxiCTR","url":"https://github.com/xue-pai/FuxiCTR"},{"title":"UlionTse/mlgb","url":"https://github.com/UlionTse/mlgb"},{"title":"Atomu2014/product-nets","url":"https://github.com/Atomu2014/product-nets"},{"title":"JianzhouZhan/Awesome-RecSystem-Models","url":"https://github.com/JianzhouZhan/Awesome-RecSystem-Models"},{"title":"tangxyw/RecAlgorithm","url":"https://github.com/tangxyw/RecAlgorithm"},{"title":"Atomu2014/product-nets-distributed","url":"https://github.com/Atomu2014/product-nets-distributed"},{"title":"wangweitong/recommend_system","url":"https://github.com/wangweitong/recommend_system"},{"title":"wangweitong/DL","url":"https://github.com/wangweitong/DL"},{"title":"jamesdvance/predicting_clicks","url":"https://github.com/jamesdvance/predicting_clicks"}]},"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/memorize-factorize-or-be-naive-learning","title":"Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction","date":"2021-08-03","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":0,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/product-based-neural-networks-for-user","title":"Product-based Neural Networks for User Response Prediction","date":"2016-11-01","rows_on_this_dataset":3,"code_links":11,"syntology":null},{"paper":"/paper/deep-learning-over-multi-field-categorical","title":"Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction","date":"2016-01-11","rows_on_this_dataset":1,"code_links":5,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":9,"samples_ran":0,"samples_unverified":9,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}