Подборка статей 2025 (часть 1)

Как и обещал, выкладываю подборку статей по темам, которые освещал на выступлении.

End2End
- OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment
- OneRec Technical Report
- OneRec-V2 Technical Report
- OneLoc: Geo-Aware Generative Recommender Systems for Local Life Service
- OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion
- OneSearch: A Preliminary Exploration of the Unified End-to-End Generative Framework for E-commerce Search
- UniSearch: Rethinking Search System with a Unified Generative Architecture
- EGA-V1: Unifying Online Advertising with End-to-End Learning
- EGA-V2: An End-to-end Generative Framework for Industrial Advertising
- GPR: Towards a Generative Pre-trained One-Model Paradigm for Large-Scale Advertising Recommendation

LLM + RecSys
- PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations
- OneRec-Think: In-Text Reasoning for Generative Recommendation
- Align3GR: Unified Multi-Level Alignment for LLM-based Generative Recommendation
- GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks

Масштабирование
- LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders
- Scaling Generative Recommendations with Context Parallelism on Hierarchical Sequential Transducers
- Twin-Flow Generative Ranking Network for Recommendation
- InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
- MARM: Unlocking the Recommendation Cache Scaling-Law through Memory Augmentation and Scalable Complexity
- TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios
- RankMixer: Scaling Up Ranking Models in Industrial Recommenders
- Climber: Toward Efficient Scaling Laws for Large Recommendation Models
- MTGR: Industrial-Scale Generative Recommendation Framework in Meituan
- Action is All You Need: Dual-Flow Generative Ranking Network for Recommendation
- Meta’s Generative Ads Model (GEM): The Central Brain Accelerating Ads Recommendation AI Innovation
- OneTrans: Unified Feature Interaction and Sequence Modeling with One Transformer in Industrial Recommender
- Massive Memorization with Hundreds of Trillions of Parameters for Sequential Transducer Generative Recommenders
- From Features to Transformers: Redefining Ranking for Scalable Impact
- From Scaling to Structured Expressivity: Rethinking Transformers for CTR Prediction
- Scaling Transformers for Discriminative Recommendation via Generative Pretraining
- Meta Lattice: Model Space Redesign for Cost-Effective Industry-Scale Ads Recommendations