{"total":278,"rows":[{"id":"2110.01889","title":"Deep Neural Networks and Tabular Data: A Survey","created":"2021-10-05","cited_by_count":491,"area":null},{"id":"2308.16505","title":"Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations","created":"2023-08-31","cited_by_count":131,"area":"agents"},{"id":"2101.11427","title":"One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction","created":"2021-01-27","cited_by_count":78,"area":null},{"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":"2403.02545","title":"Wukong: Towards a Scaling Law for Large-Scale Recommendation","created":"2024-03-04","cited_by_count":74,"area":null},{"id":"2104.13030","title":"A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation","created":"2021-04-27","cited_by_count":59,"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":"2111.06236","title":"Discovering and Explaining the Representation Bottleneck of DNNs","created":"2021-11-11","cited_by_count":42,"area":null},{"id":"2104.10584","title":"Deep Learning for Click-Through Rate Estimation","created":"2021-04-21","cited_by_count":33,"area":null},{"id":"2102.12994","title":"$FM^2$: Field-matrixed Factorization Machines for Recommender Systems","created":"2021-02-20","cited_by_count":31,"area":null},{"id":"2106.06713","title":"AutoLoss: Automated Loss Function Search in Recommendations","created":"2021-06-12","cited_by_count":31,"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":"2204.09078","title":"AutoField: Automating Feature Selection in Deep Recommender Systems","created":"2022-04-19","cited_by_count":30,"area":null},{"id":"2309.04612","title":"Self-optimizing Feature Generation via Categorical Hashing Representation and Hierarchical Reinforcement Crossing","created":"2023-09-08","cited_by_count":30,"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":"2112.07221","title":"HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework","created":"2021-12-14","cited_by_count":28,"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":"2402.09617","title":"LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations","created":"2024-02-14","cited_by_count":24,"area":null},{"id":"2110.04514","title":"Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach","created":"2021-10-09","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":"2402.06871","title":"Non-autoregressive Generative Models for Reranking Recommendation","created":"2024-02-10","cited_by_count":18,"area":null},{"id":"2410.12034","title":"A Survey on Deep Tabular Learning","created":"2024-10-15","cited_by_count":18,"area":null},{"id":"2106.02768","title":"Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction","created":"2021-06-05","cited_by_count":17,"area":null},{"id":"2408.03533","title":"Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation","created":"2024-08-07","cited_by_count":17,"area":"lr"},{"id":"2402.13750","title":"Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph","created":"2024-02-21","cited_by_count":16,"area":null},{"id":"2101.02342","title":"User Response Prediction in Online Advertising","created":"2021-01-07","cited_by_count":15,"area":null},{"id":"2305.01236","title":"CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario","created":"2023-05-02","cited_by_count":15,"area":null},{"id":"2406.14043","title":"Taxonomy-Guided Zero-Shot Recommendations with LLMs","created":"2024-06-20","cited_by_count":15,"area":null},{"id":"2407.02112","title":"A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data","created":"2024-07-02","cited_by_count":15,"area":null},{"id":"2208.12044","title":"Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Network","created":"2022-08-21","cited_by_count":14,"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":"2105.08909","title":"Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction","created":"2021-05-19","cited_by_count":13,"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":"2201.01004","title":"Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection","created":"2022-01-04","cited_by_count":13,"area":null},{"id":"2201.10980","title":"Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework","created":"2022-01-17","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":"2401.02143","title":"Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions","created":"2024-01-04","cited_by_count":13,"area":null},{"id":"2403.00793","title":"Ads Recommendation in a Collapsed and Entangled World","created":"2024-02-22","cited_by_count":13,"area":null},{"id":"2405.15783","title":"Multimodality Invariant Learning for Multimedia-Based New Item Recommendation","created":"2024-04-28","cited_by_count":13,"area":null},{"id":"2101.04852","title":"Knowledge-Enhanced Top-K Recommendation in Poincar\\'e Ball","created":"2021-01-13","cited_by_count":12,"area":null},{"id":"2104.08542","title":"ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table","created":"2021-04-17","cited_by_count":12,"area":null},{"id":"2108.11513","title":"Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer","created":"2021-08-24","cited_by_count":12,"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":"2402.06859","title":"LiRank: Industrial Large Scale Ranking Models at LinkedIn","created":"2024-02-10","cited_by_count":12,"area":null},{"id":"2102.06027","title":"STUaNet: Understanding uncertainty in spatiotemporal collective human mobility","created":"2021-02-09","cited_by_count":11,"area":null},{"id":"2202.06129","title":"RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph","created":"2022-02-12","cited_by_count":11,"area":null},{"id":"2306.03527","title":"Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao","created":"2023-06-06","cited_by_count":11,"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":"2105.14975","title":"Privileged Graph Distillation for Cold Start Recommendation","created":"2021-05-31","cited_by_count":10,"area":null},{"id":"2201.07821","title":"Building a Performance Model for Deep Learning Recommendation Model Training on GPUs","created":"2022-01-19","cited_by_count":10,"area":null},{"id":"2302.04464","title":"Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models","created":"2023-02-09","cited_by_count":10,"area":null},{"id":"2305.01245","title":"MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition","created":"2023-05-02","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":"2510.10432","title":"Hierarchical LoRA MoE for Efficient CTR Model Scaling","created":"2025-10-12","cited_by_count":10,"area":"moe"},{"id":"2301.12762","title":"Causality-based CTR Prediction using Graph Neural Networks","created":"2023-01-30","cited_by_count":9,"area":null},{"id":"2305.11615","title":"SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalization","created":"2023-05-19","cited_by_count":9,"area":null},{"id":"2308.08487","title":"Temporal Interest Network for User Response Prediction","created":"2023-08-15","cited_by_count":9,"area":null},{"id":"2502.12478","title":"MSE-Adapter: A Lightweight Plugin Endowing LLMs with the Capability to Perform Multimodal Sentiment Analysis and Emotion Recognition","created":"2025-02-18","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":"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":"2302.03487","title":"PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce","created":"2023-02-06","cited_by_count":8,"area":null},{"id":"2305.17386","title":"HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer","created":"2023-05-27","cited_by_count":8,"area":null},{"id":"2402.02334","title":"Arithmetic Feature Interaction Is Necessary for Deep Tabular Learning","created":"2024-02-04","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":"2602.10016","title":"Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design","created":"2026-02-10","cited_by_count":8,"area":null},{"id":"2107.05474","title":"Denoising User-aware Memory Network for Recommendation","created":"2021-07-12","cited_by_count":7,"area":null},{"id":"2207.07187","title":"NASRec: Weight Sharing Neural Architecture Search for Recommender Systems","created":"2022-07-14","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":"2404.08301","title":"Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty","created":"2024-04-12","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":"2508.05633","title":"KuaiLive: A Real-time Interactive Dataset for Live Streaming Recommendation","created":"2025-08-07","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":"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":"2206.14649","title":"Cooperative Retriever and Ranker in Deep Recommenders","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":"2402.15134","title":"Deep Coupling Network For Multivariate Time Series Forecasting","created":"2024-02-23","cited_by_count":6,"area":null},{"id":"2403.00877","title":"Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large-Scale Recommendation","created":"2024-03-01","cited_by_count":6,"area":null},{"id":"2408.11523","title":"LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding","created":"2024-08-21","cited_by_count":6,"area":null},{"id":"2505.19505","title":"Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model","created":"2025-05-26","cited_by_count":6,"area":"lr"},{"id":"2103.03103","title":"Interpretable Artificial Intelligence through the Lens of Feature Interaction","created":"2021-03-01","cited_by_count":5,"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":"2106.07356","title":"Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling","created":"2021-06-04","cited_by_count":5,"area":null},{"id":"2111.01786","title":"A Recommendation System to Enhance Midwives' Capacities in Low-Income Countries","created":"2021-11-02","cited_by_count":5,"area":null},{"id":"2201.05500","title":"Communication-Efficient TeraByte-Scale Model Training Framework for Online Advertising","created":"2022-01-05","cited_by_count":5,"area":null},{"id":"2209.11727","title":"Boost CTR Prediction for New Advertisements via Modeling Visual Content","created":"2022-09-23","cited_by_count":5,"area":null},{"id":"2211.02863","title":"Inductive Graph Transformer for Delivery Time Estimation","created":"2022-11-05","cited_by_count":5,"area":null},{"id":"2310.16430","title":"An Integrative Paradigm for Enhanced Stroke Prediction: Synergizing XGBoost and xDeepFM Algorithms","created":"2023-10-25","cited_by_count":5,"area":null},{"id":"2401.11648","title":"Next Visit Diagnosis Prediction via Medical Code-Centric Multimodal Contrastive EHR Modelling with Hierarchical Regularisation","created":"2024-01-22","cited_by_count":5,"area":null},{"id":"2405.00568","title":"Powering In-Database Dynamic Model Slicing for Structured Data Analytics","created":"2024-05-01","cited_by_count":5,"area":"moe"},{"id":"2502.06101","title":"RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning","created":"2025-02-10","cited_by_count":5,"area":"rag"},{"id":"2502.09928","title":"Deep Tree Tensor Networks","created":"2025-02-14","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":"2108.02191","title":"Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000$\\times$ Compression and 3.1$\\times$ Faster Inference","created":"2021-08-04","cited_by_count":4,"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":"2204.00752","title":"Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation","created":"2022-04-02","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":"2204.06240","title":"CowClip: Reducing CTR Prediction Model Training Time from 12 hours to 10 minutes on 1 GPU","created":"2022-04-13","cited_by_count":4,"area":null}],"next_cursor":"100"}