{"total":105,"rows":[{"id":"2105.02605","title":"GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph","created":"2021-05-06","cited_by_count":143,"area":null},{"id":"2202.00645","title":"Generalization Analysis of Message Passing Neural Networks on Large Random Graphs","created":"2022-02-01","cited_by_count":59,"area":null},{"id":"2104.06608","title":"Search to aggregate neighborhood for graph neural network","created":"2021-04-14","cited_by_count":48,"area":null},{"id":"2305.13084","title":"A Fractional Graph Laplacian Approach to Oversmoothing","created":"2023-05-22","cited_by_count":45,"area":null},{"id":"2202.08523","title":"Contrastive Meta Learning with Behavior Multiplicity for Recommendation","created":"2022-02-17","cited_by_count":37,"area":null},{"id":"2402.13973","title":"Linear-Time Graph Neural Networks for Scalable Recommendations","created":"2024-02-21","cited_by_count":31,"area":null},{"id":"2111.08202","title":"Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks","created":"2021-11-16","cited_by_count":24,"area":null},{"id":"2108.11333","title":"Lightweight Self-Attentive Sequential Recommendation","created":"2021-08-25","cited_by_count":22,"area":null},{"id":"2106.03819","title":"A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps","created":"2021-06-07","cited_by_count":21,"area":null},{"id":"2311.09544","title":"Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta","created":"2023-11-16","cited_by_count":21,"area":null},{"id":"2409.16670","title":"GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learning","created":"2024-09-25","cited_by_count":21,"area":null},{"id":"2204.01499","title":"FedRecAttack: Model Poisoning Attack to Federated Recommendation","created":"2022-04-01","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":"2108.11883","title":"DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN","created":"2021-08-26","cited_by_count":19,"area":null},{"id":"2304.01926","title":"High-Throughput Vector Similarity Search in Knowledge Graphs","created":"2023-04-04","cited_by_count":19,"area":null},{"id":"2101.01775","title":"Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph","created":"2021-01-05","cited_by_count":17,"area":null},{"id":"2111.05897","title":"Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters","created":"2021-11-10","cited_by_count":17,"area":null},{"id":"2303.08816","title":"Borda Regret Minimization for Generalized Linear Dueling Bandits","created":"2023-03-15","cited_by_count":17,"area":null},{"id":"2101.02342","title":"User Response Prediction in Online Advertising","created":"2021-01-07","cited_by_count":15,"area":null},{"id":"2105.07706","title":"Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach","created":"2021-05-17","cited_by_count":15,"area":null},{"id":"2103.16164","title":"Graph Intention Network for Click-through Rate Prediction in Sponsored Search","created":"2021-03-30","cited_by_count":14,"area":null},{"id":"2210.02023","title":"DreamShard: Generalizable Embedding Table Placement for Recommender Systems","created":"2022-10-05","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":"2108.01053","title":"Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders","created":"2021-08-02","cited_by_count":12,"area":null},{"id":"2108.13555","title":"Adaptive Label Smoothing To Regularize Large-Scale Graph Training","created":"2021-08-30","cited_by_count":12,"area":null},{"id":"2305.14814","title":"What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding","created":"2023-05-24","cited_by_count":12,"area":null},{"id":"2302.10872","title":"MP-Rec: Hardware-Software Co-Design to Enable Multi-Path Recommendation","created":"2023-02-21","cited_by_count":9,"area":null},{"id":"2405.18127","title":"Graph Coarsening with Message-Passing Guarantees","created":"2024-05-28","cited_by_count":9,"area":null},{"id":"2101.06855","title":"GraphAttacker: A General Multi-Task GraphAttack Framework","created":"2021-01-18","cited_by_count":8,"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":"2304.07503","title":"Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation","created":"2023-04-15","cited_by_count":8,"area":null},{"id":"2312.07861","title":"GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks","created":"2023-12-13","cited_by_count":8,"area":null},{"id":"2506.03437","title":"Quake: Adaptive Indexing for Vector Search","created":"2025-06-03","cited_by_count":8,"area":"rag"},{"id":"2305.12349","title":"PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation","created":"2023-05-21","cited_by_count":7,"area":null},{"id":"2411.00970","title":"Incremental IVF Index Maintenance for Streaming Vector Search","created":"2024-11-01","cited_by_count":7,"area":null},{"id":"2105.07752","title":"Explicit Semantic Cross Feature Learning via Pre-trained Graph Neural Networks for CTR Prediction","created":"2021-05-17","cited_by_count":6,"area":null},{"id":"2201.10382","title":"On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systems","created":"2022-01-24","cited_by_count":6,"area":null},{"id":"2209.11679","title":"Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation","created":"2022-09-22","cited_by_count":6,"area":null},{"id":"2504.05573","title":"MicroNN: An On-device Disk-resident Updatable Vector Database","created":"2025-04-08","cited_by_count":6,"area":"rag"},{"id":"2504.11182","title":"Exploring Backdoor Attack and Defense for LLM-empowered Recommendations","created":"2025-04-15","cited_by_count":6,"area":"safety"},{"id":"2205.02446","title":"Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services","created":"2022-05-05","cited_by_count":5,"area":null},{"id":"2308.12767","title":"On the Consistency of Average Embeddings for Item Recommendation","created":"2023-08-24","cited_by_count":5,"area":null},{"id":"2401.02827","title":"Let's Get It Started: Fostering the Discoverability of New Releases on Deezer","created":"2024-01-05","cited_by_count":5,"area":null},{"id":"2405.05231","title":"DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training","created":"2024-05-08","cited_by_count":5,"area":null},{"id":"2407.04272","title":"Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression","created":"2024-07-05","cited_by_count":5,"area":null},{"id":"2102.12029","title":"Theoretical Understandings of Product Embedding for E-commerce Machine Learning","created":"2021-02-24","cited_by_count":4,"area":null},{"id":"2202.11525","title":"GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction","created":"2022-02-21","cited_by_count":4,"area":null},{"id":"2208.07704","title":"QuickSkill: Novice Skill Estimation in Online Multiplayer Games","created":"2022-08-15","cited_by_count":4,"area":null},{"id":"2211.09925","title":"FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning","created":"2022-11-17","cited_by_count":4,"area":null},{"id":"2305.01801","title":"When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?","created":"2023-05-02","cited_by_count":4,"area":null},{"id":"2310.08039","title":"Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models","created":"2023-10-12","cited_by_count":4,"area":null},{"id":"2401.09507","title":"Deep Ensemble Shape Calibration: Multi-Field Post-hoc Calibration in Online Advertising","created":"2024-01-17","cited_by_count":4,"area":null},{"id":"2402.07025","title":"Generalization Error of Graph Neural Networks in the Mean-field Regime","created":"2024-02-10","cited_by_count":4,"area":null},{"id":"2402.14399","title":"Ensure Timeliness and Accuracy: A Novel Sliding Window Data Stream Paradigm for Live Streaming Recommendation","created":"2024-02-22","cited_by_count":4,"area":null},{"id":"2406.17335","title":"A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems","created":"2024-06-25","cited_by_count":4,"area":null},{"id":"2407.15452","title":"GraphScale: A Framework to Enable Machine Learning over Billion-node Graphs","created":"2024-07-22","cited_by_count":4,"area":null},{"id":"2504.02112","title":"PolyG: Adaptive Graph Traversal for Diverse GraphRAG Questions","created":"2025-04-02","cited_by_count":4,"area":null},{"id":"2506.00808","title":"Unlearning Inversion Attacks for Graph Neural Networks","created":"2025-06-01","cited_by_count":4,"area":null},{"id":"2310.19394","title":"LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation","created":"2023-10-30","cited_by_count":3,"area":null},{"id":"2406.04805","title":"GENIE: Watermarking Graph Neural Networks for Link Prediction","created":"2024-06-07","cited_by_count":3,"area":null},{"id":"2506.11743","title":"Taxonomy of reduction matrices for Graph Coarsening","created":"2025-06-13","cited_by_count":3,"area":null},{"id":"2511.15122","title":"Multi-Aspect Cross-modal Quantization for Generative Recommendation","created":"2025-11-19","cited_by_count":3,"area":null},{"id":"2105.05881","title":"A Graph Neural Network Approach for Product Relationship Prediction","created":"2021-05-12","cited_by_count":2,"area":null},{"id":"2202.02698","title":"Triangle Graph Interest Network for Click-through Rate Prediction","created":"2022-02-06","cited_by_count":2,"area":null},{"id":"2203.06467","title":"G$^3$SR: Global Graph Guided Session-based Recommendation","created":"2022-03-12","cited_by_count":2,"area":null},{"id":"2207.06652","title":"Everyone's Preference Changes Differently: Weighted Multi-Interest Retrieval Model","created":"2022-07-14","cited_by_count":2,"area":null},{"id":"2211.10002","title":"Influential Recommender System","created":"2022-11-18","cited_by_count":2,"area":null},{"id":"2211.14982","title":"Two Is Better Than One: Dual Embeddings for Complementary Product Recommendations","created":"2022-11-28","cited_by_count":2,"area":null},{"id":"2303.15702","title":"Distributed Graph Embedding with Information-Oriented Random Walks","created":"2023-03-28","cited_by_count":2,"area":null},{"id":"2308.01329","title":"EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding","created":"2023-08-02","cited_by_count":2,"area":null},{"id":"2603.01590","title":"IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshu","created":"2026-03-02","cited_by_count":2,"area":null},{"id":"2604.22881","title":"MTServe: Efficient Serving for Generative Recommendation Models with Hierarchical Caches","created":"2026-04-24","cited_by_count":2,"area":null},{"id":"2103.06474","title":"Metapaths guided Neighbors aggregated Network for?Heterogeneous Graph Reasoning","created":"2021-03-11","cited_by_count":1,"area":null},{"id":"2108.06452","title":"AdaGNN: A multi-modal latent representation meta-learner for GNNs based on AdaBoosting","created":"2021-08-14","cited_by_count":1,"area":null},{"id":"2109.10602","title":"Context-aware Tree-based Deep Model for Recommender Systems","created":"2021-09-22","cited_by_count":1,"area":null},{"id":"2112.01035","title":"Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems","created":"2021-12-02","cited_by_count":1,"area":null},{"id":"2201.09189","title":"Hardware/Software Co-Programmable Framework for Computational SSDs to Accelerate Deep Learning Service on Large-Scale Graphs","created":"2022-01-23","cited_by_count":1,"area":null},{"id":"2205.09489","title":"Spatial Autoregressive Coding for Graph Neural Recommendation","created":"2022-05-19","cited_by_count":1,"area":null},{"id":"2302.08128","title":"Neighbor Based Enhancement for the Long-Tail Ranking Problem in Video Rank Models","created":"2023-02-16","cited_by_count":1,"area":null},{"id":"2304.04330","title":"Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?","created":"2023-04-09","cited_by_count":1,"area":null},{"id":"2312.01476","title":"Optimizing Context-Enhanced Relational Joins","created":"2023-12-03","cited_by_count":1,"area":null},{"id":"2401.03114","title":"GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs","created":"2024-01-06","cited_by_count":1,"area":null},{"id":"2406.14635","title":"Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments","created":"2024-06-20","cited_by_count":1,"area":null},{"id":"2601.02662","title":"When Prompting Meets Spiking: Graph Sparse Prompting via Spiking Graph Prompt Learning","created":"2026-01-06","cited_by_count":1,"area":null},{"id":"2603.21653","title":"MISApp: Multi-Hop Intent-Aware Session Graph Learning for Next App Prediction","created":"2026-03-23","cited_by_count":1,"area":null},{"id":"2606.14929","title":"Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Experts","created":"2026-06-12","cited_by_count":1,"area":null},{"id":"2112.08986","title":"A Heterogeneous Graph Learning Model for Cyber-Attack Detection","created":"2021-12-16","cited_by_count":0,"area":null},{"id":"2209.15238","title":"Efficient Graph based Recommender System with Weighted Averaging of Messages","created":"2022-09-30","cited_by_count":0,"area":null},{"id":"2211.06365","title":"Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates","created":"2022-11-11","cited_by_count":0,"area":null},{"id":"2302.06126","title":"Expediting Distributed DNN Training with Device Topology-Aware Graph Deployment","created":"2023-02-13","cited_by_count":0,"area":null},{"id":"2302.14395","title":"Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up","created":"2023-02-28","cited_by_count":0,"area":null},{"id":"2304.05474","title":"Audience Expansion for Multi-show Release Based on an Edge-prompted Heterogeneous Graph Network","created":"2023-04-08","cited_by_count":0,"area":null},{"id":"2309.11741","title":"Unveiling Optimal SDG Pathways: An Innovative Approach Leveraging Graph Pruning and Intent Graph for Effective Recommendations","created":"2023-09-21","cited_by_count":0,"area":null},{"id":"2402.16073","title":"Pfeed: Generating near real-time personalized feeds using precomputed embedding similarities","created":"2024-02-25","cited_by_count":0,"area":null},{"id":"2406.04488","title":"Negative Feedback for Music Personalization","created":"2024-06-06","cited_by_count":0,"area":null},{"id":"2408.00799","title":"Deep Uncertainty-Based Explore for Index Construction and Retrieval in Recommendation System","created":"2024-07-22","cited_by_count":0,"area":null},{"id":"2502.20497","title":"Creator-Side Recommender System: Challenges, Designs, and Applications","created":"2025-02-25","cited_by_count":0,"area":null},{"id":"2505.14310","title":"Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity","created":"2025-05-20","cited_by_count":0,"area":null},{"id":"2511.05582","title":"Uncertainty Modeling for Multi-Objective RTA Interception with Distillation Acceleration","created":"2025-11-05","cited_by_count":0,"area":null},{"id":"2511.20564","title":"E2E-GRec: An End-to-End Joint Training Framework for Graph Neural Networks and Recommender Systems","created":"2025-11-25","cited_by_count":0,"area":null}],"next_cursor":"100"}