{"url":"/sota/sequential-recommendation-on-yelp","task":{"name":"Sequential Recommendation","url":"/task/sequential-recommendation","note":null},"dataset":{"name":"Yelp","url":"/dataset/yelp"},"category":"Miscellaneous","categories":["Graphs","Knowledge Base","Miscellaneous"],"category_note":null,"description":"Sequential recommendation is a sophisticated approach to providing personalized suggestions by analyzing users' historical interactions in a sequential manner. Unlike traditional recommendation systems, which consider items in isolation, sequential recommendation takes into account the temporal order of user actions. This method is particularly valuable in domains where the sequence of events matters, such as streaming services, e-commerce platforms, and social media.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["HR@5","HR@10","HR@20","NDCG@5","NDCG@10","NDCG@20","HR@5 (99 Neg. Samples)","HR@10 (99 Neg. Samples)","NDCG@5 (99 Neg. Samples)","NDCG@10 (99 Neg. Samples)","MRR (99 Neg. Samples)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"HR@5":null,"HR@10":null,"HR@20":null,"NDCG@5":"higher","NDCG@10":"higher","NDCG@20":"higher","HR@5 (99 Neg. Samples)":null,"HR@10 (99 Neg. Samples)":null,"NDCG@5 (99 Neg. Samples)":"higher","NDCG@10 (99 Neg. Samples)":"higher","MRR (99 Neg. Samples)":"higher"}},"counts":{"rows":1,"rows_with_code":1,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"BSARec","metrics":{"HR@10":"0.0465","HR@10 (99 Neg. Samples)":"0.7848","HR@20":"0.0746","HR@5":"0.0275","HR@5 (99 Neg. Samples)":"0.6447","MRR (99 Neg. Samples)":"0.4587","NDCG@10":"0.0231","NDCG@10 (99 Neg. Samples)":"0.5280","NDCG@20":"0.0302","NDCG@5":"0.0170","NDCG@5 (99 Neg. Samples)":"0.4824"},"uses_additional_data":false,"paper_date":"2023-12-16","paper":"/paper/an-attentive-inductive-bias-for-sequential","paper_url":"https://arxiv.org/abs/2312.10325v2","paper_title":"An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention","code":"https://github.com/yehjin-shin/bsarec","n_code_links":2,"syntology":{"n_ran":7,"n_unverified":0,"n_samples":7,"n_pointer_only_licence":7}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":7,"n_unverified":0,"n_samples":7,"n_pointer_only_licence":7,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":7,"n_unverified":0,"n_samples":7,"n_pointer_only_licence":7,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}