{"url":"/dataset/yelp2018","name":"Yelp2018","full_name":"Yelp2018","description_markdown":"The Yelp2018 dataset is adopted from the 2018 edition of the yelp challenge. Wherein local businesses like restaurants and bars are viewed as items. We use the same 10-core setting in order to ensure data quality.","description_withheld":null,"homepage":"","introduced_date":"2019-05-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/neural-graph-collaborative-filtering","title":"Neural Graph Collaborative Filtering","first_author":"Xiang Wang","url":null},"license":null,"modalities":[],"tasks":[{"name":"Link Prediction","url":"/task/link-prediction","datasets_with_task":"/datasets/task/link-prediction"},{"name":"Recommendation Systems","url":"/task/recommendation-systems","datasets_with_task":"/datasets/task/recommendation-systems"},{"name":"Collaborative Filtering","url":"/task/collaborative-filtering","datasets_with_task":"/datasets/task/collaborative-filtering"},{"name":"Unsupervised Text Style Transfer","url":"/task/unsupervised-text-style-transfer","datasets_with_task":"/datasets/task/unsupervised-text-style-transfer"}],"languages":[],"variants":["Yelp2018"],"data_loaders":[{"repo":"https://github.com/MRahimii/graph-recommender-system","url":"https://github.com/MRahimii/graph-recommender-system","frameworks":["pytorch"]}],"num_papers_in_archive":125,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/recommendation-systems-on-yelp2018","task":"Recommendation Systems","dataset_variant":"Yelp2018","rows":11,"metrics":["NDCG@20","Recall@20","HR@10","HR@100","PSP@10","nDCG@10","nDCG@100"],"first_row_in_archive_order":{"model":"NESCL","paper":"/paper/neighborhood-enhanced-supervised-contrastive","metrics":{"NDCG@20":"0.0611","Recall@20":"0.0743"},"code_links":[{"title":"PeiJieSun/NESCL","url":"https://github.com/PeiJieSun/NESCL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/collaborative-filtering-on-yelp2018","task":"Collaborative Filtering","dataset_variant":"Yelp2018","rows":9,"metrics":["NDCG@20","Recall@20"],"first_row_in_archive_order":{"model":"NESCL","paper":"/paper/neighborhood-enhanced-supervised-contrastive","metrics":{"NDCG@20":"0.0611","Recall@20":"0.0743"},"code_links":[{"title":"PeiJieSun/NESCL","url":"https://github.com/PeiJieSun/NESCL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/why-is-normalization-necessary-for-linear","title":"Why is Normalization Necessary for Linear Recommenders?","date":"2025-04-08","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/svd-ae-simple-autoencoders-for-collaborative","title":"SVD-AE: Simple Autoencoders for Collaborative Filtering","date":"2024-05-08","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/turbo-cf-matrix-decomposition-free-graph","title":"Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation","date":"2024-04-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/neighborhood-enhanced-supervised-contrastive","title":"Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering","date":"2024-02-18","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/perturbation-recovery-method-for","title":"Blurring-Sharpening Process Models for Collaborative Filtering","date":"2022-11-17","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/sapling-similarity-outperforms-other-local","title":"Sapling Similarity: a performing and interpretable memory-based tool for recommendation","date":"2022-10-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/mgdcf-distance-learning-via-markov-graph","title":"MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering","date":"2022-04-05","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/ultragcn-ultra-simplification-of-graph","title":"UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation","date":"2021-10-28","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/simplex-a-simple-and-strong-baseline-for","title":"SimpleX: A Simple and Strong Baseline for Collaborative Filtering","date":"2021-09-26","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/lt-ocf-learnable-time-ode-based-collaborative","title":"LT-OCF: Learnable-Time ODE-based Collaborative Filtering","date":"2021-08-08","rows_on_this_dataset":2,"code_links":2,"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."}},{"paper":"/paper/self-supervised-graph-learning-for","title":"Self-supervised Graph Learning for Recommendation","date":"2020-10-21","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/lightgcn-simplifying-and-powering-graph","title":"LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation","date":"2020-02-06","rows_on_this_dataset":2,"code_links":18,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":2,"samples_unverified":5,"pointer_only_for_licence":2,"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":6,"samples_harvested":21,"samples_ran":9,"samples_unverified":12,"pointer_only_for_licence":2,"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."}