{"url":"/sota/recommendation-systems-on-flixster-monti","task":{"name":"Recommendation Systems","url":"/task/recommendation-systems","note":null},"dataset":{"name":"Flixster Monti","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"],"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"}},"counts":{"rows":7,"rows_with_code":7,"rows_with_paper_page":7,"rows_dated":7,"rows_using_additional_data":5},"rows":[{"rank_in_archive_order":1,"model":"IGMC","metrics":{"RMSE":"0.872"},"uses_additional_data":false,"paper_date":"2019-04-26","paper":"/paper/inductive-graph-pattern-learning-for","paper_url":"https://arxiv.org/abs/1904.12058v3","paper_title":"Inductive Matrix Completion Based on Graph Neural Networks","code":"https://github.com/muhanzhang/IGMC","n_code_links":3,"syntology":{"n_ran":1,"n_unverified":1,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":2,"model":"MG-GAT","metrics":{"RMSE":"0.876"},"uses_additional_data":true,"paper_date":"2020-09-20","paper":"/paper/interpretable-recommender-system-with","paper_url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3696092","paper_title":"Interpretable Recommender System With Heterogeneous Information: A Geometric Deep Learning Perspective","code":"https://github.com/zuirod/mg-gat","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"GRAEM","metrics":{"RMSE":"0.8857"},"uses_additional_data":true,"paper_date":"2019-08-25","paper":"/paper/scalable-probabilistic-matrix-factorization","paper_url":"https://arxiv.org/abs/1908.09393v2","paper_title":"Scalable Probabilistic Matrix Factorization with Graph-Based Priors","code":"https://github.com/strahl2e/GPMF-GBP-AAAI-20","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"Factorized EAE","metrics":{"RMSE":"0.908"},"uses_additional_data":false,"paper_date":"2018-03-07","paper":"/paper/deep-models-of-interactions-across-sets","paper_url":"http://arxiv.org/abs/1803.02879v2","paper_title":"Deep Models of Interactions Across Sets","code":"https://github.com/mravanba/deep_exchangeable_tensors","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"GC-MC","metrics":{"RMSE":"0.917"},"uses_additional_data":true,"paper_date":"2017-06-07","paper":"/paper/graph-convolutional-matrix-completion","paper_url":"http://arxiv.org/abs/1706.02263v2","paper_title":"Graph Convolutional Matrix Completion","code":"https://github.com/dmlc/dgl/tree/master/examples/mxnet/_deprecated/gcmc","n_code_links":17,"syntology":null},{"rank_in_archive_order":6,"model":"sRGCNN","metrics":{"RMSE":"0.9258"},"uses_additional_data":true,"paper_date":"2017-04-22","paper":"/paper/geometric-matrix-completion-with-recurrent","paper_url":"http://arxiv.org/abs/1704.06803v1","paper_title":"Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks","code":"https://github.com/fmonti/mgcnn","n_code_links":2,"syntology":null},{"rank_in_archive_order":7,"model":"GRALS","metrics":{"RMSE":"1.2447"},"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}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. 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