{"total":45,"rows":[{"id":"2403.02545","title":"Wukong: Towards a Scaling Law for Large-Scale Recommendation","created":"2024-03-04","cited_by_count":74,"area":null},{"id":"2310.04400","title":"On the Embedding Collapse when Scaling up Recommendation Models","created":"2023-10-06","cited_by_count":29,"area":null},{"id":"2107.01830","title":"ARM-Net: Adaptive Relation Modeling Network for Structured Data","created":"2021-07-05","cited_by_count":24,"area":null},{"id":"2212.00522","title":"CL4CTR: A Contrastive Learning Framework for CTR Prediction","created":"2022-12-01","cited_by_count":14,"area":null},{"id":"2105.11866","title":"GraphFM: Graph Factorization Machines for Feature Interaction Modeling","created":"2021-05-25","cited_by_count":13,"area":null},{"id":"2203.12267","title":"PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation","created":"2022-03-23","cited_by_count":12,"area":null},{"id":"2402.06859","title":"LiRank: Industrial Large Scale Ranking Models at LinkedIn","created":"2024-02-10","cited_by_count":12,"area":null},{"id":"2310.19453","title":"FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction","created":"2023-10-30","cited_by_count":11,"area":null},{"id":"2209.15409","title":"Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions","created":"2022-09-30","cited_by_count":7,"area":null},{"id":"2408.17162","title":"Deep Feature Embedding for Tabular Data","created":"2024-08-30","cited_by_count":7,"area":null},{"id":"2206.13764","title":"Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems","created":"2022-06-28","cited_by_count":6,"area":null},{"id":"2402.14230","title":"MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systems","created":"2024-02-22","cited_by_count":6,"area":null},{"id":"2105.08649","title":"DCAP: Deep Cross Attentional Product Network for User Response Prediction","created":"2021-05-18","cited_by_count":5,"area":null},{"id":"2505.14057","title":"Field Matters: A Lightweight LLM-enhanced Method for CTR Prediction","created":"2025-05-20","cited_by_count":4,"area":null},{"id":"2206.08661","title":"Boosting Factorization Machines via Saliency-Guided Mixup","created":"2022-06-17","cited_by_count":3,"area":null},{"id":"2505.17999","title":"Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction","created":"2025-05-23","cited_by_count":3,"area":null},{"id":"2604.13737","title":"TokenFormer: Unify the Multi-Field and Sequential Recommendation Worlds","created":"2026-04-15","cited_by_count":3,"area":null},{"id":"2307.09321","title":"Exploiting Field Dependencies for Learning on Categorical Data","created":"2023-07-18","cited_by_count":2,"area":null},{"id":"2403.17740","title":"All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction","created":"2024-03-26","cited_by_count":2,"area":null},{"id":"2104.07553","title":"Efficient Click-Through Rate Prediction for Developing Countries via Tabular Learning","created":"2021-04-15","cited_by_count":1,"area":null},{"id":"2111.04983","title":"Dynamic Parameterized Network for CTR Prediction","created":"2021-11-09","cited_by_count":1,"area":null},{"id":"2209.08353","title":"Human Pose Driven Object Effects Recommendation","created":"2022-09-17","cited_by_count":1,"area":null},{"id":"2308.02568","title":"Weighted Multi-Level Feature Factorization for App ads CTR and installation prediction","created":"2023-08-03","cited_by_count":1,"area":null},{"id":"2408.08713","title":"Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Prediction","created":"2024-08-16","cited_by_count":1,"area":null},{"id":"2501.15816","title":"AdaF^2M^2: Comprehensive Learning and Responsive Leveraging Features in Recommendation System","created":"2025-01-27","cited_by_count":1,"area":null},{"id":"2503.14813","title":"Scaled Supervision is an Implicit Lipschitz Regularizer","created":"2025-03-19","cited_by_count":1,"area":null},{"id":"2605.10886","title":"LoKA: Low-precision Kernel Applications for Recommendation Models At Scale","created":"2026-05-11","cited_by_count":1,"area":null},{"id":"2107.01326","title":"SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing","created":"2021-07-03","cited_by_count":0,"area":null},{"id":"2210.16080","title":"RESUS: Warm-Up Cold Users via Meta-Learning Residual User Preferences in CTR Prediction","created":"2022-10-28","cited_by_count":0,"area":null},{"id":"2305.04891","title":"DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction","created":"2023-05-03","cited_by_count":0,"area":null},{"id":"2308.09202","title":"A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings","created":"2023-08-17","cited_by_count":0,"area":null},{"id":"2309.08159","title":"AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness","created":"2023-09-15","cited_by_count":0,"area":null},{"id":"2405.18146","title":"Unified Low-rank Compression Framework for Click-through Rate Prediction","created":"2024-05-28","cited_by_count":0,"area":null},{"id":"2406.03892","title":"Polyhedral Conic Classifier for CTR Prediction","created":"2024-06-06","cited_by_count":0,"area":null},{"id":"2411.09286","title":"A Centralized-Distributed Transfer Model for Cross-Domain Recommendation Based on Multi-Source Heterogeneous Transfer Learning","created":"2024-11-14","cited_by_count":0,"area":null},{"id":"2412.08198","title":"Adaptive$^2$: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling","created":"2024-12-11","cited_by_count":0,"area":null},{"id":"2502.03417","title":"From Features to Transformers: Redefining Ranking for Scalable Impact","created":"2025-02-05","cited_by_count":0,"area":null},{"id":"2502.13471","title":"From Features to Graphs: Exploring Graph Structures and Pairwise Interactions via GNNs","created":"2025-02-19","cited_by_count":0,"area":null},{"id":"2505.02844","title":"Feature Staleness Aware Incremental Learning for CTR Prediction","created":"2025-04-29","cited_by_count":0,"area":null},{"id":"2512.01179","title":"Toward a benchmark for CTR prediction in online advertising: datasets, evaluation protocols and perspectives","created":"2025-12-01","cited_by_count":0,"area":null},{"id":"2512.10370","title":"LLM-Empowered Representation Learning for Emerging Item Recommendation","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2602.00002","title":"Disentangled Interest Network for Out-of-Distribution CTR Prediction","created":"2025-11-14","cited_by_count":0,"area":null},{"id":"2602.03223","title":"Distribution-Aware End-to-End Embedding for Streaming Numerical Features in Click-Through Rate Prediction","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2602.09194","title":"ML-DCN: Masked Low-Rank Deep Crossing Network Towards Scalable Ads Click-through Rate Prediction at Pinterest","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2609.37905","title":"Beyond Interaction Capacity: Estimator Scaling with Recursive Models for CTR Prediction","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}