{"total":126,"rows":[{"id":"2402.17152","title":"Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations","created":"2024-02-27","cited_by_count":188,"area":null},{"id":"2308.14296","title":"RecMind: Large Language Model Powered Agent For Recommendation","created":"2023-08-28","cited_by_count":129,"area":"agents"},{"id":"2308.11131","title":"ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation","created":"2023-08-22","cited_by_count":77,"area":null},{"id":"2108.04468","title":"End-to-End User Behavior Retrieval in Click-Through RatePrediction Model","created":"2021-08-10","cited_by_count":45,"area":null},{"id":"2403.06447","title":"CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation","created":"2024-03-11","cited_by_count":44,"area":"lr"},{"id":"2105.08489","title":"Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising","created":"2021-05-18","cited_by_count":39,"area":null},{"id":"2110.11154","title":"Personalized Transfer of User Preferences for Cross-domain Recommendation","created":"2021-10-21","cited_by_count":34,"area":null},{"id":"2104.10584","title":"Deep Learning for Click-Through Rate Estimation","created":"2021-04-21","cited_by_count":33,"area":null},{"id":"2504.12722","title":"SimUSER: Simulating User Behavior with Large Language Models for Recommender System Evaluation","created":"2025-04-17","cited_by_count":33,"area":null},{"id":"2202.10462","title":"Click-Through Rate Prediction in Online Advertising: A Literature Review","created":"2022-02-22","cited_by_count":31,"area":null},{"id":"2310.09234","title":"ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction","created":"2023-10-13","cited_by_count":27,"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":"2110.06475","title":"SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios","created":"2021-10-13","cited_by_count":23,"area":null},{"id":"2204.06519","title":"CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention","created":"2022-04-04","cited_by_count":22,"area":null},{"id":"2101.04526","title":"Measuring Recommender System Effects with Simulated Users","created":"2021-01-12","cited_by_count":20,"area":null},{"id":"2310.18608","title":"Embedding in Recommender Systems: A Survey","created":"2023-10-28","cited_by_count":20,"area":null},{"id":"2109.13527","title":"Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation","created":"2021-09-28","cited_by_count":18,"area":null},{"id":"2101.02342","title":"User Response Prediction in Online Advertising","created":"2021-01-07","cited_by_count":15,"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":"2306.04039","title":"Revisiting Neural Retrieval on Accelerators","created":"2023-06-06","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":"2201.13311","title":"Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer","created":"2022-01-25","cited_by_count":13,"area":null},{"id":"2306.14462","title":"Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation","created":"2023-06-26","cited_by_count":13,"area":null},{"id":"2203.11014","title":"DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction","created":"2022-03-11","cited_by_count":12,"area":null},{"id":"2205.14315","title":"Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition","created":"2022-05-28","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":"2201.04399","title":"RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems","created":"2022-01-12","cited_by_count":10,"area":null},{"id":"2308.01737","title":"MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction","created":"2023-08-03","cited_by_count":10,"area":null},{"id":"2105.04067","title":"Neural Graph Matching based Collaborative Filtering","created":"2021-05-10","cited_by_count":9,"area":null},{"id":"2301.12762","title":"Causality-based CTR Prediction using Graph Neural Networks","created":"2023-01-30","cited_by_count":9,"area":null},{"id":"2109.09587","title":"Recommender systems based on graph embedding techniques: A comprehensive review","created":"2021-09-20","cited_by_count":8,"area":null},{"id":"2208.06089","title":"Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge","created":"2022-08-12","cited_by_count":8,"area":null},{"id":"2209.05016","title":"FiBiNet++: Reducing Model Size by Low Rank Feature Interaction Layer for CTR Prediction","created":"2022-09-12","cited_by_count":8,"area":null},{"id":"2411.09852","title":"InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction","created":"2024-11-15","cited_by_count":8,"area":null},{"id":"2109.00217","title":"Multi-Sample based Contrastive Loss for Top-k Recommendation","created":"2021-09-01","cited_by_count":7,"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":"2306.16425","title":"A Collaborative Transfer Learning Framework for Cross-domain Recommendation","created":"2023-06-26","cited_by_count":7,"area":null},{"id":"2104.10907","title":"XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction","created":"2021-04-22","cited_by_count":6,"area":null},{"id":"2107.13472","title":"Reenvisioning Collaborative Filtering vs Matrix Factorization","created":"2021-07-28","cited_by_count":6,"area":null},{"id":"2109.11654","title":"Modeling Dynamic Attributes for Next Basket Recommendation","created":"2021-09-23","cited_by_count":6,"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":"2601.00930","title":"AlignUSER: Human-Aligned LLM Agents via World Models for Recommender System Evaluation","created":"2026-01-02","cited_by_count":6,"area":"wm"},{"id":"2402.00299","title":"Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction","created":"2024-02-01","cited_by_count":5,"area":null},{"id":"2104.01716","title":"Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling","created":"2021-04-05","cited_by_count":4,"area":null},{"id":"2105.05563","title":"Looking at CTR Prediction Again: Is Attention All You Need?","created":"2021-05-12","cited_by_count":4,"area":null},{"id":"2505.11995","title":"Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation","created":"2025-05-17","cited_by_count":4,"area":"rag"},{"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":"2110.13587","title":"Arbitrary Distribution Modeling with Censorship in Real-Time Bidding Advertising","created":"2021-10-26","cited_by_count":3,"area":null},{"id":"2201.05861","title":"Deep Unified Representation for Heterogeneous Recommendation","created":"2022-01-15","cited_by_count":3,"area":null},{"id":"2206.08661","title":"Boosting Factorization Machines via Saliency-Guided Mixup","created":"2022-06-17","cited_by_count":3,"area":null},{"id":"2209.12212","title":"Efficient Long Sequential User Data Modeling for Click-Through Rate Prediction","created":"2022-09-25","cited_by_count":3,"area":null},{"id":"2308.14902","title":"Ad-Rec: Advanced Feature Interactions to Address Covariate-Shifts in Recommendation Networks","created":"2023-08-28","cited_by_count":3,"area":null},{"id":"2410.20730","title":"GPRec: Bi-level User Modeling for Deep Recommenders","created":"2024-10-28","cited_by_count":3,"area":null},{"id":"2411.15731","title":"Fusion Matters: Learning Fusion in Deep Click-through Rate Prediction Models","created":"2024-11-24","cited_by_count":3,"area":null},{"id":"2505.16752","title":"Action is All You Need: Dual-Flow Generative Ranking Network for Recommendation","created":"2025-05-22","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":"2604.09549","title":"Beyond Offline A/B Testing: Context-Aware Agent Simulation for Recommender System Evaluation","created":"2026-01-26","cited_by_count":3,"area":"agents"},{"id":"2107.12025","title":"ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding","created":"2021-07-26","cited_by_count":2,"area":null},{"id":"2109.07571","title":"Secure Your Ride: Real-time Matching Success Rate Prediction for Passenger-Driver Pairs","created":"2021-09-14","cited_by_count":2,"area":null},{"id":"2110.09770","title":"AEFE: Automatic Embedded Feature Engineering for Categorical Features","created":"2021-10-19","cited_by_count":2,"area":null},{"id":"2112.15292","title":"Neural Hierarchical Factorization Machines for User's Event Sequence Analysis","created":"2021-12-31","cited_by_count":2,"area":null},{"id":"2209.07574","title":"Towards a Better Microcredit Decision","created":"2022-08-23","cited_by_count":2,"area":null},{"id":"2210.15154","title":"AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling","created":"2022-10-27","cited_by_count":2,"area":null},{"id":"2211.01334","title":"MemoNet: Memorizing All Cross Features' Representations Efficiently via Multi-Hash Codebook Network for CTR Prediction","created":"2022-10-25","cited_by_count":2,"area":null},{"id":"2301.01089","title":"xDeepInt: a hybrid architecture for modeling the vector-wise and bit-wise feature interactions","created":"2023-01-03","cited_by_count":2,"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":"2402.02718","title":"Denoising Time Cycle Modeling for Recommendation","created":"2024-02-05","cited_by_count":2,"area":null},{"id":"2406.00011","title":"DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation","created":"2024-05-20","cited_by_count":2,"area":null},{"id":"2507.12202","title":"Sparse Autoencoders for Sequential Recommendation Models: Interpretation and Flexible Control","created":"2025-07-16","cited_by_count":2,"area":"mechinterp"},{"id":"2104.01924","title":"DexDeepFM: Ensemble Diversity Enhanced Extreme Deep Factorization Machine Model","created":"2021-04-05","cited_by_count":1,"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":"2206.14647","title":"Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction","created":"2022-06-28","cited_by_count":1,"area":null},{"id":"2208.01375","title":"BERT4Loc: BERT for Location -- POI Recommender System","created":"2022-08-02","cited_by_count":1,"area":null},{"id":"2212.12533","title":"Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising","created":"2022-12-06","cited_by_count":1,"area":null},{"id":"2307.10244","title":"Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors","created":"2023-07-17","cited_by_count":1,"area":null},{"id":"2404.18304","title":"Retrieval-Oriented Knowledge for Click-Through Rate Prediction","created":"2024-04-28","cited_by_count":1,"area":null},{"id":"2408.00166","title":"Review of Explainable Graph-Based Recommender Systems","created":"2024-07-31","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":"2408.11302","title":"Modeling Reference-dependent Choices with Graph Neural Networks","created":"2024-08-21","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":"2510.02215","title":"Improving Large-Scale Recommender Systems with Auxiliary Learning","created":"2025-10-02","cited_by_count":1,"area":null},{"id":"2103.06105","title":"BCFNet: A Balanced Collaborative Filtering Network with Attention Mechanism","created":"2021-03-10","cited_by_count":0,"area":null},{"id":"2104.12483","title":"Represent Items by Items: An Enhanced Representation of the Target Item for Recommendation","created":"2021-04-26","cited_by_count":0,"area":null},{"id":"2106.15400","title":"Online Interaction Detection for Click-Through Rate Prediction","created":"2021-06-27","cited_by_count":0,"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":"2107.03690","title":"Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)","created":"2021-07-08","cited_by_count":0,"area":null},{"id":"2107.12024","title":"Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction","created":"2021-07-26","cited_by_count":0,"area":null},{"id":"2108.03989","title":"Spatial-Temporal Deep Intention Destination Networks for Online Travel Planning","created":"2021-08-09","cited_by_count":0,"area":null},{"id":"2108.07421","title":"Memory-Efficient Factorization Machines via Binarizing both Data and Model Coefficients","created":"2021-08-17","cited_by_count":0,"area":null},{"id":"2110.10205","title":"MultiHead MultiModal Deep Interest Recommendation Network","created":"2021-10-19","cited_by_count":0,"area":null},{"id":"2201.11936","title":"Consistent Collaborative Filtering via Tensor Decomposition","created":"2022-01-28","cited_by_count":0,"area":null},{"id":"2208.13330","title":"Time-aware Self-Attention Meets Logic Reasoning in Recommender Systems","created":"2022-08-29","cited_by_count":0,"area":null},{"id":"2209.02256","title":"Making the black-box brighter: interpreting machine learning algorithm for forecasting drilling accidents","created":"2022-09-06","cited_by_count":0,"area":null},{"id":"2210.10725","title":"SML:Enhance the Network Smoothness with Skip Meta Logit for CTR Prediction","created":"2022-10-09","cited_by_count":0,"area":null},{"id":"2304.12549","title":"COUPA: An Industrial Recommender System for Online to Offline Service Platforms","created":"2023-04-25","cited_by_count":0,"area":null},{"id":"2308.02571","title":"ADRNet: A Generalized Collaborative Filtering Framework Combining Clinical and Non-Clinical Data for Adverse Drug Reaction Prediction","created":"2023-08-03","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":"2310.20705","title":"Farthest Greedy Path Sampling for Two-shot Recommender Search","created":"2023-10-31","cited_by_count":0,"area":null}],"next_cursor":"100"}