{"url":"/sota/collaborative-filtering-on-douban","task":{"name":"Recommendation Systems","url":"/task/recommendation-systems","note":null},"dataset":{"name":"Douban","url":"/dataset/douban"},"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","NDCG","Recall@20","AUC","HR@10","HR@100","PSP@10","nDCG@10","nDCG@100"],"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","NDCG":"higher","Recall@20":"higher","AUC":"higher","HR@10":null,"HR@100":null,"PSP@10":null,"nDCG@10":"higher","nDCG@100":"higher"}},"counts":{"rows":7,"rows_with_code":6,"rows_with_paper_page":7,"rows_dated":7,"rows_using_additional_data":1},"rows":[{"rank_in_archive_order":1,"model":"I-CFN","metrics":{"RMSE":"0.6911"},"uses_additional_data":false,"paper_date":"2016-06-24","paper":"/paper/hybrid-recommender-system-based-on","paper_url":"http://arxiv.org/abs/1606.07659v3","paper_title":"Hybrid Recommender System based on Autoencoders","code":"https://github.com/fstrub95/Autoencoders_cf","n_code_links":4,"syntology":null},{"rank_in_archive_order":2,"model":"U-CFN","metrics":{"RMSE":"0.7049"},"uses_additional_data":false,"paper_date":"2016-06-24","paper":"/paper/hybrid-recommender-system-based-on","paper_url":"http://arxiv.org/abs/1606.07659v3","paper_title":"Hybrid Recommender System based on Autoencoders","code":"https://github.com/fstrub95/Autoencoders_cf","n_code_links":4,"syntology":null},{"rank_in_archive_order":3,"model":"GRALS","metrics":{"RMSE":"0.714"},"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":4,"model":"FedPerGNN","metrics":{"RMSE":"0.775"},"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":5,"model":"FedGNN","metrics":{"RMSE":"0.79"},"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":6,"model":"DGRec","metrics":{"NDCG":"0.195","Recall@20":"0.1861"},"uses_additional_data":false,"paper_date":"2019-02-25","paper":"/paper/session-based-social-recommendation-via","paper_url":"http://arxiv.org/abs/1902.09362v2","paper_title":"Session-based Social Recommendation via Dynamic Graph Attention Networks","code":"https://github.com/DeepGraphLearning/RecommenderSystems","n_code_links":2,"syntology":{"n_ran":1,"n_unverified":12,"n_samples":13,"n_pointer_only_licence":0}},{"rank_in_archive_order":7,"model":"∞-AE","metrics":{"AUC":"0.9523","HR@10":"0.2356","HR@100":"0.2837","PSP@10":"0.0128","nDCG@10":"0.2494","nDCG@100":"0.2326"},"uses_additional_data":false,"paper_date":"2022-06-03","paper":"/paper/infinite-recommendation-networks-a-data","paper_url":"https://arxiv.org/abs/2206.02626v3","paper_title":"Infinite Recommendation Networks: A Data-Centric Approach","code":"https://github.com/Guang000/Awesome-Dataset-Distillation","n_code_links":5,"syntology":{"n_ran":6,"n_unverified":7,"n_samples":13,"n_pointer_only_licence":0}}],"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. 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