Imagen de Portada

LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Jia, Jian, Wang, Yipei, Li, Yan, Chen, Honggang, Bai, Xuehan, Liu, Zhaocheng, Liang, Jian, Chen, Quan, Li, Han, Jiang, Peng, Gai, Kun
Formato:	Preprint
Publicado:	2024
Materias:	Information Retrieval Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

From Principles to Applications: A Comprehensive Survey of Discrete Tokenizers in Generation, Comprehension, Recommendation, and Information Retrieval
por: Jia, Jian, et al.
Publicado: (2025)

Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction
por: Fan, Zhongxiang, et al.
Publicado: (2024)

BBQRec: Behavior-Bind Quantization for Multi-Modal Sequential Recommendation
por: Li, Kaiyuan, et al.
Publicado: (2025)

Modeling User Fatigue for Sequential Recommendation
por: Li, Nian, et al.
Publicado: (2024)

Large Language Model as Universal Retriever in Industrial-Scale Recommender System
por: Jiang, Junguang, et al.
Publicado: (2025)

Selection and Exploitation of High-Quality Knowledge from Large Language Models for Recommendation
por: Wang, Guanchen, et al.
Publicado: (2025)

TokenMixer-Large: Scaling Up Large Ranking Models in Industrial Recommenders
por: Jiang, Yuchen, et al.
Publicado: (2026)

RecFlow: An Industrial Full Flow Recommendation Dataset
por: Liu, Qi, et al.
Publicado: (2024)

Towards Comprehensible Recommendation with Large Language Model Fine-tuning
por: Luo, Yunze, et al.
Publicado: (2025)

Creator-Side Recommender System: Challenges, Designs, and Applications
por: Chen, Xiaoshuang, et al.
Publicado: (2025)

Behavior-Guided Candidate Calibration for Multimodal Recommendation
por: Li, Zesheng, et al.
Publicado: (2026)

MTGR: Industrial-Scale Generative Recommendation Framework in Meituan
por: Han, Ruidong, et al.
Publicado: (2025)

Evaluating Conversational Recommender Systems via Large Language Models: A User-Centric Framework
por: Chen, Nuo, et al.
Publicado: (2025)

Future Impact Decomposition in Request-level Recommendations
por: Wang, Xiaobei, et al.
Publicado: (2024)

Generative Recommendation for Large-Scale Advertising
por: Xue, Ben, et al.
Publicado: (2026)

R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems
por: Gu, Hao, et al.
Publicado: (2025)

SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems
por: Jia, Pengyue, et al.
Publicado: (2024)

Representation Quantization for Collaborative Filtering Augmentation
por: Luo, Yunze, et al.
Publicado: (2025)

LEMUR: Large scale End-to-end MUltimodal Recommendation
por: Han, Xintian, et al.
Publicado: (2025)

Reinforced Preference Optimization for Reasoning-Augmented Recommendations
por: Gao, Jingtong, et al.
Publicado: (2026)

Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers
por: Zhang, Zhiyang, et al.
Publicado: (2026)

DADF: A Distribution-Aware Debiasing Framework for Watch-Time Regression in Recommender Systems
por: Yang, Yiqing, et al.
Publicado: (2026)

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples
por: Xu, Xiaoxiao, et al.
Publicado: (2025)

MTFM: A Scalable and Alignment-free Foundation Model for Industrial Recommendation in Meituan
por: Song, Xin, et al.
Publicado: (2026)

AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems
por: Xue, Zhenghai, et al.
Publicado: (2023)

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
por: He, Tiantian, et al.
Publicado: (2025)

Coarse-to-fine Dynamic Uplift Modeling for Real-time Video Recommendation
por: Meng, Chang, et al.
Publicado: (2024)

MaRI: Accelerating Ranking Model Inference via Structural Re-parameterization in Large Scale Recommendation System
por: Huang, Yusheng, et al.
Publicado: (2026)

SMES: Towards Scalable Multi-Task Recommendation via Expert Sparsity
por: Zhang, Yukun, et al.
Publicado: (2026)

Parallel Latent Reasoning for Sequential Recommendation
por: Tang, Jiakai, et al.
Publicado: (2026)

Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey
por: Liu, Qijiong, et al.
Publicado: (2024)

RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm
por: Zhang, Yabin, et al.
Publicado: (2024)

PROMISE: Process Reward Models Unlock Test-Time Scaling Laws in Generative Recommendations
por: Guo, Chengcheng, et al.
Publicado: (2026)

CHIME: A Compressive Framework for Holistic Interest Modeling
por: Bai, Yong, et al.
Publicado: (2025)

Denoising Neural Reranker for Recommender Systems
por: Mao, Wenyu, et al.
Publicado: (2025)

Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
por: Liu, Ziru, et al.
Publicado: (2024)

DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System
por: Ye, Wencai, et al.
Publicado: (2025)

RecSys Arena: Pair-wise Recommender System Evaluation with Large Language Models
por: Wu, Zhuo, et al.
Publicado: (2024)

Bidirectional Knowledge Distillation for Enhancing Sequential Recommendation with Large Language Models
por: Wu, Jiongran, et al.
Publicado: (2025)

Improving Conversational Recommendation with Contextual Adaptation of External Recommenders and LLM-based Reranking
por: Li, Chuang, et al.
Publicado: (2025)