{"url":"/dataset/retailrocket","name":"Retailrocket","full_name":null,"description_markdown":"The dataset consists of three files: a file with behaviour data (events.csv), a file with item properties (itemproperties.сsv) and a file, which describes category tree (categorytree.сsv). The data has been collected from a real-world ecommerce website. It is raw data, i.e. without any content transformations, however, all values are hashed due to confidential issues. The purpose of publishing is to motivate researches in the field of recommender systems with implicit feedback.","description_withheld":null,"homepage":"https://www.kaggle.com/retailrocket/ecommerce-dataset","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Session-Based Recommendations","url":"/task/session-based-recommendations","datasets_with_task":"/datasets/task/session-based-recommendations"}],"languages":[],"variants":["Retailrocket"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/session-based-recommendations-on-retailrocket","task":"Session-Based Recommendations","dataset_variant":"Retailrocket","rows":2,"metrics":["MRR@20","Hit@20"],"first_row_in_archive_order":{"model":"EMDE MM","paper":"/paper/an-efficient-manifold-density-estimator-for","metrics":{"Hit@20":"0.5073","MRR@20":"0.3664"},"code_links":[{"title":"Synerise/booking-challenge","url":"https://github.com/Synerise/booking-challenge"},{"title":"Synerise/kdd-cup-2021","url":"https://github.com/Synerise/kdd-cup-2021"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/an-efficient-manifold-density-estimator-for","title":"An efficient manifold density estimator for all recommendation systems","date":"2020-06-02","rows_on_this_dataset":2,"code_links":2,"syntology":null}],"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."}