{"total":24,"rows":[{"id":"2209.10117","title":"A Comprehensive Survey on Trustworthy Recommender Systems","created":"2022-09-21","cited_by_count":32,"area":null},{"id":"2307.15967","title":"Graph Condensation for Inductive Node Representation Learning","created":"2023-07-29","cited_by_count":31,"area":null},{"id":"2401.11720","title":"Graph Condensation: A Survey","created":"2024-01-22","cited_by_count":23,"area":null},{"id":"2302.10900","title":"Semi-decentralized Federated Ego Graph Learning for Recommendation","created":"2023-02-10","cited_by_count":22,"area":null},{"id":"2310.18608","title":"Embedding in Recommender Systems: A Survey","created":"2023-10-28","cited_by_count":20,"area":null},{"id":"2406.06596","title":"Are Large Language Models the New Interface for Data Pipelines?","created":"2024-06-06","cited_by_count":13,"area":null},{"id":"2403.12660","title":"ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems","created":"2024-03-19","cited_by_count":9,"area":null},{"id":"2408.08585","title":"OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction","created":"2024-08-16","cited_by_count":8,"area":null},{"id":"2402.02855","title":"Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation","created":"2024-02-05","cited_by_count":7,"area":null},{"id":"2204.00281","title":"i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender Systems","created":"2022-04-01","cited_by_count":4,"area":null},{"id":"2204.01390","title":"A Comprehensive Survey on Automated Machine Learning for Recommendations","created":"2022-04-04","cited_by_count":4,"area":null},{"id":"2307.11004","title":"Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering","created":"2023-07-12","cited_by_count":3,"area":null},{"id":"2402.04453","title":"The Potential of AutoML for Recommender Systems","created":"2024-02-06","cited_by_count":3,"area":null},{"id":"2401.04408","title":"Fine-Grained Embedding Dimension Optimization During Training for Recommender Systems","created":"2024-01-09","cited_by_count":1,"area":null},{"id":"2402.11641","title":"Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models","created":"2024-02-18","cited_by_count":1,"area":null},{"id":"2508.06208","title":"Graph Federated Learning for Personalized Privacy Recommendation","created":"2025-08-08","cited_by_count":1,"area":null},{"id":"2309.01552","title":"OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking","created":"2023-09-04","cited_by_count":0,"area":null},{"id":"2311.05864","title":"DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops","created":"2023-11-10","cited_by_count":0,"area":null},{"id":"2312.11018","title":"Hypergraph-Enhanced Dual Convolutional Network for Bundle Recommendation","created":"2023-12-18","cited_by_count":0,"area":null},{"id":"2407.19937","title":"AOTree: Aspect Order Tree-based Model for Explainable Recommendation","created":"2024-07-29","cited_by_count":0,"area":null},{"id":"2501.18126","title":"HyperZero: A Customized End-to-End Auto-Tuning System for Recommendation with Hourly Feedback","created":"2025-01-30","cited_by_count":0,"area":null},{"id":"2506.03391","title":"Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks","created":"2025-06-03","cited_by_count":0,"area":null},{"id":"2506.23314","title":"Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance","created":"2025-06-29","cited_by_count":0,"area":null},{"id":"2606.01111","title":"LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems","created":"2026-05-31","cited_by_count":0,"area":null}],"next_cursor":null}