{"url":"/sota/recommendation-systems-on-redial","task":{"name":"Recommendation Systems","url":"/task/recommendation-systems","note":null},"dataset":{"name":"ReDial","url":"/dataset/redial"},"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":["Recall@1","Recall@10","Recall@50"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Recall@1":"higher","Recall@10":"higher","Recall@50":"higher"}},"counts":{"rows":8,"rows_with_code":7,"rows_with_paper_page":8,"rows_dated":7,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"KERL","metrics":{"Recall@1":"0.056","Recall@10":"0.217","Recall@50":"0.426"},"uses_additional_data":false,"paper_date":"2023-12-18","paper":"/paper/knowledge-graphs-and-pre-trained-language","paper_url":"https://arxiv.org/abs/2312.10967v3","paper_title":"Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems","code":"https://github.com/icedpanda/KERL","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"C2CRS","metrics":{"Recall@1":"0.053","Recall@10":"0.233","Recall@50":"0.407"},"uses_additional_data":false,"paper_date":"2022-01-04","paper":"/paper/c2-crs-coarse-to-fine-contrastive-learning","paper_url":"https://arxiv.org/abs/2201.02732v3","paper_title":"C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System","code":"https://github.com/rucaibox/wsdm2022-c2crs","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"UniCRS","metrics":{"Recall@1":"0.051","Recall@10":"0.224","Recall@50":"0.428"},"uses_additional_data":false,"paper_date":"2022-06-19","paper":"/paper/towards-unified-conversational-recommender","paper_url":"https://arxiv.org/abs/2206.09363v1","paper_title":"Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning","code":"https://github.com/rucaibox/unicrs","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"CRFR","metrics":{"Recall@1":"0.04","Recall@10":"0.202","Recall@50":"0.399"},"uses_additional_data":false,"paper_date":null,"paper":"/paper/crfr-improving-conversational-recommender","paper_url":"https://aclanthology.org/2021.emnlp-main.355","paper_title":"CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":5,"model":"CR-Walker","metrics":{"Recall@1":"0.04","Recall@10":"0.187","Recall@50":"0.376"},"uses_additional_data":false,"paper_date":"2020-10-20","paper":"/paper/bridging-the-gap-between-conversational","paper_url":"https://arxiv.org/abs/2010.10333v2","paper_title":"CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation","code":"https://github.com/truthless11/CR-Walker","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"KGSF","metrics":{"Recall@1":"0.039","Recall@10":"0.183","Recall@50":"0.378"},"uses_additional_data":false,"paper_date":"2020-07-08","paper":"/paper/improving-conversational-recommender-systems","paper_url":"https://arxiv.org/abs/2007.04032v1","paper_title":"Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion","code":"https://github.com/Lancelot39/KGSF","n_code_links":2,"syntology":null},{"rank_in_archive_order":7,"model":"KBRD","metrics":{"Recall@1":"0.03","Recall@10":"0.163","Recall@50":"0.338"},"uses_additional_data":false,"paper_date":"2019-08-15","paper":"/paper/towards-knowledge-based-recommender-dialog","paper_url":"https://arxiv.org/abs/1908.05391v2","paper_title":"Towards Knowledge-Based Recommender Dialog System","code":"https://github.com/THUDM/KBRD","n_code_links":1,"syntology":null},{"rank_in_archive_order":8,"model":"UCCR","metrics":{"Recall@10":"0.2161","Recall@50":"0.4258"},"uses_additional_data":false,"paper_date":"2022-04-20","paper":"/paper/user-centric-conversational-recommendation","paper_url":"https://arxiv.org/abs/2204.09263v2","paper_title":"User-Centric Conversational Recommendation with Multi-Aspect User Modeling","code":"https://github.com/lisk123/uccr","n_code_links":1,"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. 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