{"total":57,"rows":[{"id":"2306.08121","title":"Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations","created":"2023-06-13","cited_by_count":52,"area":null},{"id":"2306.00248","title":"TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest","created":"2023-05-31","cited_by_count":35,"area":null},{"id":"2503.02453","title":"Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations","created":"2025-03-04","cited_by_count":35,"area":null},{"id":"2402.11139","title":"LiGNN: Graph Neural Networks at LinkedIn","created":"2024-02-17","cited_by_count":31,"area":null},{"id":"2305.15498","title":"Large Language Models for User Interest Journeys","created":"2023-05-24","cited_by_count":24,"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":"2405.03988","title":"LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application","created":"2024-05-07","cited_by_count":21,"area":null},{"id":"2209.08435","title":"Rethinking Personalized Ranking at Pinterest: An End-to-End Approach","created":"2022-09-18","cited_by_count":16,"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":"2507.16289","title":"Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders","created":"2025-07-22","cited_by_count":12,"area":null},{"id":"2508.15281","title":"MMQ: Multimodal Mixture-of-Quantization Tokenization for Semantic ID Generation and User Behavioral Adaptation","created":"2025-08-21","cited_by_count":10,"area":null},{"id":"2510.22049","title":"Massive Memorization with Hundreds of Trillions of Parameters for Sequential Transducer Generative Recommenders","created":"2025-10-24","cited_by_count":10,"area":null},{"id":"2504.10507","title":"PinRec: Unified Generative Retrieval for Pinterest Recommender Systems","created":"2025-04-09","cited_by_count":8,"area":null},{"id":"2506.00450","title":"DV365: Extremely Long User History Modeling at Instagram","created":"2025-05-31","cited_by_count":8,"area":null},{"id":"2507.12704","title":"PinFM: Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform","created":"2025-07-17","cited_by_count":8,"area":null},{"id":"2411.10057","title":"KuaiFormer: Transformer-Based Retrieval at Kuaishou","created":"2024-11-15","cited_by_count":6,"area":null},{"id":"2508.05640","title":"Request-Only Optimization for Recommendation Systems","created":"2025-07-24","cited_by_count":5,"area":null},{"id":"2601.20083","title":"LLaTTE: Scaling Laws for Multi-Stage Sequence Modeling in Large-Scale Ads Recommendation","created":"2026-01-27","cited_by_count":5,"area":null},{"id":"2302.01255","title":"adSformers: Personalization from Short-Term Sequences and Diversity of Representations in Etsy Ads","created":"2023-02-02","cited_by_count":4,"area":null},{"id":"2504.17811","title":"OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning","created":"2025-04-22","cited_by_count":4,"area":null},{"id":"2511.14865","title":"FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications","created":"2025-11-18","cited_by_count":4,"area":null},{"id":"2408.11372","title":"Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation","created":"2024-08-21","cited_by_count":3,"area":null},{"id":"2409.02856","title":"Building a Scalable, Effective, and Steerable Search and Ranking Platform","created":"2024-09-04","cited_by_count":3,"area":null},{"id":"2409.12972","title":"TRACE: Transformer-based user Representations from Attributed Clickstream Event sequences","created":"2024-09-02","cited_by_count":3,"area":null},{"id":"2411.13322","title":"Scaling Laws for Online Advertisement Retrieval","created":"2024-11-20","cited_by_count":3,"area":null},{"id":"2504.08169","title":"On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction","created":"2025-04-10","cited_by_count":3,"area":null},{"id":"2505.21811","title":"Revisiting Self-attention for Cross-domain Sequential Recommendation","created":"2025-05-27","cited_by_count":3,"area":null},{"id":"2312.03217","title":"Rethinking E-Commerce Search","created":"2023-12-06","cited_by_count":2,"area":null},{"id":"2504.06609","title":"InteractRank: Personalized Web-Scale Search Pre-Ranking with Cross Interaction Features","created":"2025-04-09","cited_by_count":2,"area":null},{"id":"2508.09789","title":"Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations","created":"2025-08-13","cited_by_count":2,"area":"mllm"},{"id":"2508.18700","title":"Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training","created":"2025-08-26","cited_by_count":2,"area":null},{"id":"2511.18013","title":"Save, Revisit, Retain: A Scalable Framework for Enhancing User Retention in Large-Scale Recommender Systems","created":"2025-11-22","cited_by_count":2,"area":null},{"id":"2601.19501","title":"Masked Diffusion Generative Recommendation","created":"2026-01-27","cited_by_count":2,"area":"diffusion"},{"id":"2305.13399","title":"Efficient Large-Scale Visual Representation Learning And Evaluation","created":"2023-05-22","cited_by_count":1,"area":null},{"id":"2403.13344","title":"USE: Dynamic User Modeling with Stateful Sequence Models","created":"2024-03-20","cited_by_count":1,"area":null},{"id":"2408.09380","title":"ELASTIC: Efficient Linear Attention for Sequential Interest Compression","created":"2024-08-18","cited_by_count":1,"area":null},{"id":"2512.16581","title":"Abacus: Self-Supervised Event Counting-Aligned Distributional Pretraining for Sequential User Modeling","created":"2025-12-18","cited_by_count":1,"area":null},{"id":"2607.22518","title":"PinEqualizer: Full Funnel Content Exploration and Debiasing System at Pinterest","created":"2026-07-24","cited_by_count":1,"area":null},{"id":"2401.09724","title":"Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance","created":"2024-01-18","cited_by_count":0,"area":null},{"id":"2508.04711","title":"Scaling Generative Recommendations with Context Parallelism on Hierarchical Sequential Transducers","created":"2025-07-23","cited_by_count":0,"area":null},{"id":"2508.14948","title":"Large Foundation Model for Ads Recommendation","created":"2025-08-20","cited_by_count":0,"area":null},{"id":"2508.16793","title":"Bootstrapping Conditional Retrieval for User-to-Item Recommendations","created":"2025-08-22","cited_by_count":0,"area":null},{"id":"2509.04337","title":"Decoupled Entity Representation Learning for Pinterest Ads Ranking","created":"2025-09-04","cited_by_count":0,"area":null},{"id":"2510.04375","title":"Adaptive Weighted Loss for Sequential Recommendations on Sparse Domains","created":"2025-10-05","cited_by_count":0,"area":null},{"id":"2604.01036","title":"Aligning Recommendations with User Popularity Preferences","created":"2026-04-01","cited_by_count":0,"area":null},{"id":"2604.07739","title":"Efficient Dataset Selection for Continual Adaptation of Generative Recommenders","created":"2026-04-09","cited_by_count":0,"area":null},{"id":"2604.17259","title":"HORIZON: A Benchmark for In-the-wild User Behaviour Modeling","created":"2026-04-19","cited_by_count":0,"area":null},{"id":"2605.09040","title":"UxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2606.19108","title":"JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2607.09955","title":"A Foundation Model for Multimodal Event Sequences in Financial Applications","created":"2026-07-10","cited_by_count":0,"area":null},{"id":"2607.19253","title":"Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks","created":"2026-07-21","cited_by_count":0,"area":null},{"id":"2607.24845","title":"REPREC: Representation Driven Parameter-Efficient Recommendation System","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2607.28019","title":"Building a User Foundation Model for the Open Web","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2607.28971","title":"Don't Contrast the Impossible: Region-Constrained Batching for Contrastive User Modeling on a Local Community Platform","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2609.08443","title":"SequenceO1: End-to-End Ultra-Long (100K) Sequence Modeling in Recommendation with Low-Rank Caching","created":"2026-09-08","cited_by_count":0,"area":null},{"id":"2609.32215","title":"DP-Rec: Towards Dynamic Patching for Efficient Long-Sequence Recommendation","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.35285","title":"Textual User Taste: Natural-Language User Context for Foundation-Model Recommender System at Scale","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}