{"url":"/sota/collaborative-filtering-on-flixster","task":{"name":"Recommendation Systems","url":"/task/recommendation-systems","note":null},"dataset":{"name":"Flixster","url":null},"category":"Miscellaneous","categories":["Graphs","Knowledge Base","Miscellaneous"],"category_note":null,"description":"### **Recommendation System in AI Research**  \r\n\r\nA **Recommendation System** is a specialized AI-driven model that analyzes user preferences and behaviors to suggest relevant content, products, or services. It is widely used in domains like e-commerce, streaming platforms, social media, and personalized learning.  \r\n\r\nAI research in recommendation systems focuses on:  \r\n- **Collaborative Filtering**: Predicting user preferences based on similar users' choices.  \r\n- **Content-Based Filtering**: Recommending items based on user history and item characteristics.  \r\n- **Hybrid Models**: Combining multiple techniques for better accuracy.  \r\n- **Deep Learning & Transformers**: Using neural networks and self-attention mechanisms for personalized recommendations.  \r\n- **Graph-Based Approaches**: Leveraging knowledge graphs for relationship-aware recommendations.  \r\n\r\nKey challenges include data sparsity, scalability, and bias mitigation. Cutting-edge research explores reinforcement learning, explainability, and privacy-preserving methods to enhance recommendation systems.","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":["RMSE","Hits@10","Hits@20","nDCG@10","nDCG@20"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"RMSE":"lower","Hits@10":null,"Hits@20":null,"nDCG@10":"higher","nDCG@20":"higher"}},"counts":{"rows":4,"rows_with_code":3,"rows_with_paper_page":4,"rows_dated":4,"rows_using_additional_data":1},"rows":[{"rank_in_archive_order":1,"model":"GRALS","metrics":{"RMSE":"0.845"},"uses_additional_data":true,"paper_date":"2015-12-01","paper":"/paper/collaborative-filtering-with-graph","paper_url":"http://papers.nips.cc/paper/5938-collaborative-filtering-with-graph-information-consistency-and-scalable-methods","paper_title":"Collaborative Filtering with Graph Information: Consistency and Scalable Methods","code":"https://github.com/rofuyu/exp-grmf-nips15","n_code_links":2,"syntology":null},{"rank_in_archive_order":2,"model":"FedPerGNN","metrics":{"RMSE":"0.980"},"uses_additional_data":false,"paper_date":"2022-06-02","paper":"/paper/a-federated-graph-neural-network-framework","paper_url":"https://www.nature.com/articles/s41467-022-30714-9","paper_title":"A federated graph neural network framework for privacy-preserving personalization","code":"https://github.com/wuch15/fedpergnn","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"FedGNN","metrics":{"RMSE":"0.989"},"uses_additional_data":false,"paper_date":"2021-02-09","paper":"/paper/fedgnn-federated-graph-neural-network-for","paper_url":"https://arxiv.org/abs/2102.04925v2","paper_title":"FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":4,"model":"TransCF","metrics":{"Hits@10":"0.7309","Hits@20":"0.8374","nDCG@10":"0.4986","nDCG@20":"0.5257"},"uses_additional_data":false,"paper_date":"2019-06-04","paper":"/paper/collaborative-translational-metric-learning","paper_url":"https://arxiv.org/abs/1906.01637v1","paper_title":"Collaborative Translational Metric Learning","code":"https://github.com/hkuds/recdiff","n_code_links":2,"syntology":null}],"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":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"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"}}}