{"total":58,"rows":[{"id":"2110.03611","title":"Adversarial Retriever-Ranker for dense text retrieval","created":"2021-10-07","cited_by_count":83,"area":null},{"id":"2404.00579","title":"A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)","created":"2024-03-31","cited_by_count":58,"area":null},{"id":"2211.09324","title":"Blurring-Sharpening Process Models for Collaborative Filtering","created":"2022-11-17","cited_by_count":35,"area":null},{"id":"2103.10693","title":"Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation","created":"2021-03-19","cited_by_count":28,"area":null},{"id":"2406.17605","title":"NativE: Multi-modal Knowledge Graph Completion in the Wild","created":"2024-03-28","cited_by_count":23,"area":null},{"id":"2105.06323","title":"Bootstrapping User and Item Representations for One-Class Collaborative Filtering","created":"2021-05-13","cited_by_count":22,"area":null},{"id":"2101.11206","title":"Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection","created":"2021-01-27","cited_by_count":21,"area":null},{"id":"2109.02444","title":"Top-N Recommendation with Counterfactual User Preference Simulation","created":"2021-09-02","cited_by_count":21,"area":null},{"id":"2302.03472","title":"On the Theories Behind Hard Negative Sampling for Recommendation","created":"2023-02-07","cited_by_count":18,"area":null},{"id":"2105.09605","title":"Learning Robust Recommenders through Cross-Model Agreement","created":"2021-05-20","cited_by_count":15,"area":null},{"id":"2402.17238","title":"Does Negative Sampling Matter? A Review with Insights into its Theory and Applications","created":"2024-02-27","cited_by_count":15,"area":null},{"id":"2407.06992","title":"Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective","created":"2024-07-09","cited_by_count":15,"area":null},{"id":"2206.11549","title":"Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling","created":"2022-06-23","cited_by_count":14,"area":null},{"id":"2205.00904","title":"Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion","created":"2022-05-02","cited_by_count":13,"area":null},{"id":"2409.00342","title":"AdaNAT: Exploring Adaptive Policy for Token-Based Image Generation","created":"2024-08-31","cited_by_count":13,"area":null},{"id":"2102.13302","title":"Variation Control and Evaluation for Generative SlateRecommendations","created":"2021-02-26","cited_by_count":10,"area":null},{"id":"2107.10457","title":"Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack","created":"2021-07-22","cited_by_count":10,"area":null},{"id":"2109.03150","title":"Recommendation Fairness: From Static to Dynamic","created":"2021-09-05","cited_by_count":10,"area":null},{"id":"2110.14844","title":"From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems","created":"2021-10-28","cited_by_count":9,"area":null},{"id":"2305.16163","title":"PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation","created":"2023-05-11","cited_by_count":9,"area":null},{"id":"2504.01541","title":"Hyperbolic Diffusion Recommender Model","created":"2025-04-02","cited_by_count":9,"area":null},{"id":"2109.05773","title":"Fast Variational AutoEncoder with Inverted Multi-Index for Collaborative Filtering","created":"2021-09-13","cited_by_count":8,"area":null},{"id":"2308.00535","title":"Graph Contrastive Learning with Generative Adversarial Network","created":"2023-08-01","cited_by_count":8,"area":null},{"id":"2312.10110","title":"Enhancing Cognitive Diagnosis using Un-interacted Exercises: A Collaboration-aware Mixed Sampling Approach","created":"2023-12-15","cited_by_count":7,"area":null},{"id":"2107.10449","title":"Improve Learning from Crowds via Generative Augmentation","created":"2021-07-22","cited_by_count":6,"area":null},{"id":"2111.11026","title":"Learning Explicit User Interest Boundary for Recommendation","created":"2021-11-22","cited_by_count":6,"area":null},{"id":"2206.14649","title":"Cooperative Retriever and Ranker in Deep Recommenders","created":"2022-06-28","cited_by_count":6,"area":null},{"id":"2404.03354","title":"A Comprehensive Survey on Self-Supervised Learning for Recommendation","created":"2024-04-04","cited_by_count":6,"area":null},{"id":"2306.10047","title":"Neighborhood-based Hard Negative Mining for Sequential Recommendation","created":"2023-06-12","cited_by_count":5,"area":null},{"id":"2405.00287","title":"SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation","created":"2024-05-01","cited_by_count":4,"area":null},{"id":"2204.02011","title":"ELECRec: Training Sequential Recommenders as Discriminators","created":"2022-04-05","cited_by_count":3,"area":null},{"id":"2208.03645","title":"Generating Negative Samples for Sequential Recommendation","created":"2022-08-07","cited_by_count":3,"area":null},{"id":"2301.08479","title":"Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance","created":"2023-01-20","cited_by_count":3,"area":null},{"id":"2106.15504","title":"GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs","created":"2021-06-29","cited_by_count":2,"area":null},{"id":"2201.08042","title":"GAN-based Matrix Factorization for Recommender Systems","created":"2022-01-20","cited_by_count":2,"area":null},{"id":"2207.01762","title":"PReGAN: Answer Oriented Passage Ranking with Weakly Supervised GAN","created":"2022-07-05","cited_by_count":2,"area":null},{"id":"2306.15905","title":"Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering","created":"2023-06-28","cited_by_count":2,"area":null},{"id":"2506.11999","title":"Generative Representational Learning of Foundation Models for Recommendation","created":"2025-06-13","cited_by_count":2,"area":null},{"id":"2112.01160","title":"Learning Robust Recommender from Noisy Implicit Feedback","created":"2021-12-02","cited_by_count":1,"area":null},{"id":"2201.01815","title":"An Evaluation Study of Generative Adversarial Networks for Collaborative Filtering","created":"2022-01-05","cited_by_count":1,"area":null},{"id":"2204.06520","title":"Bayesian Negative Sampling for Recommendation","created":"2022-04-02","cited_by_count":1,"area":null},{"id":"2305.16391","title":"Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems","created":"2023-05-25","cited_by_count":1,"area":null},{"id":"2306.06851","title":"UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization","created":"2023-06-12","cited_by_count":1,"area":null},{"id":"2308.13541","title":"Adversarial Collaborative Filtering for Free","created":"2023-08-20","cited_by_count":1,"area":null},{"id":"2403.03993","title":"Personalized Negative Reservoir for Incremental Learning in Recommender Systems","created":"2024-03-06","cited_by_count":1,"area":null},{"id":"2403.10487","title":"Stimulate the Potential of Robots via Competition","created":"2024-03-15","cited_by_count":1,"area":null},{"id":"2105.04769","title":"Scalable Personalised Item Ranking through Parametric Density Estimation","created":"2021-05-11","cited_by_count":0,"area":null},{"id":"2110.06637","title":"Knowledge Graph-enhanced Sampling for Conversational Recommender System","created":"2021-10-13","cited_by_count":0,"area":null},{"id":"2201.10711","title":"Sparsity Regularization For Cold-Start Recommendation","created":"2022-01-26","cited_by_count":0,"area":null},{"id":"2206.03851","title":"Reconsidering Learning Objectives in Unbiased Recommendation with Unobserved Confounders","created":"2022-06-07","cited_by_count":0,"area":null},{"id":"2301.12400","title":"HeroNet: A Hybrid Retrieval-Generation Network for Conversational Bots","created":"2023-01-29","cited_by_count":0,"area":null},{"id":"2408.00326","title":"Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity","created":"2024-08-01","cited_by_count":0,"area":null},{"id":"2502.08161","title":"MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural Network for Recommendation","created":"2025-02-12","cited_by_count":0,"area":null},{"id":"2507.20578","title":"Beyond Interactions: Node-Level Graph Generation for Knowledge-Free Augmentation in Recommender Systems","created":"2025-07-28","cited_by_count":0,"area":null},{"id":"2510.02726","title":"PGMEL: Policy Gradient-based Generative Adversarial Network for Multimodal Entity Linking","created":"2025-10-03","cited_by_count":0,"area":null},{"id":"2602.22521","title":"TFPS: A Temporal Filtration-enhanced Positive Sample Set Construction Method for Implicit Collaborative Filtering","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2607.00448","title":"Real-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2608.10983","title":"TimeRoute: Time-Aware Modality Routing and Diffusion for Multi-Modal Recommendation","created":"2026-08-11","cited_by_count":0,"area":null}],"next_cursor":null}