{"url":"/sota/recommendation-systems-on-wechat","task":{"name":"Recommendation Systems","url":"/task/recommendation-systems","note":null},"dataset":{"name":"WeChat","url":"/dataset/wechat"},"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":["AUC","P@10"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"AUC":"higher","P@10":null}},"counts":{"rows":2,"rows_with_code":1,"rows_with_paper_page":2,"rows_dated":2,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"DANSER","metrics":{"AUC":"0.8165","P@10":"0.0823"},"uses_additional_data":false,"paper_date":"2019-03-25","paper":"/paper/dual-graph-attention-networks-for-deep-latent","paper_url":"http://arxiv.org/abs/1903.10433v1","paper_title":"Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems","code":"https://github.com/echo740/DANSER-WWW-19","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"NSCR (Wang et al., 2017)","metrics":{"AUC":"0.7727","P@10":"0.0736"},"uses_additional_data":false,"paper_date":"2017-06-10","paper":"/paper/item-silk-road-recommending-items-from","paper_url":"http://arxiv.org/abs/1706.03205v1","paper_title":"Item Silk Road: Recommending Items from Information Domains to Social Users","code":null,"n_code_links":0,"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"}}}