Guardado en:
| Autores principales: | Kang, Li, Zhao, Yuhan, Chen, Li |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.17290 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Can Large Language Models Assess Serendipity in Recommender Systems?
por: Tokutake, Yu, et al.
Publicado: (2024)
por: Tokutake, Yu, et al.
Publicado: (2024)
A Universal Framework for Offline Serendipity Evaluation in Recommender Systems via Large Language Models
por: Tokutake, Yu, et al.
Publicado: (2025)
por: Tokutake, Yu, et al.
Publicado: (2025)
Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
por: Xi, Yunjia, et al.
Publicado: (2025)
por: Xi, Yunjia, et al.
Publicado: (2025)
Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models
por: Yong, Qian, et al.
Publicado: (2025)
por: Yong, Qian, et al.
Publicado: (2025)
Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
por: Chen, Weixin, et al.
Publicado: (2025)
por: Chen, Weixin, et al.
Publicado: (2025)
Engineering Serendipity through Recommendations of Items with Atypical Aspects
por: Aditya, Ramit, et al.
Publicado: (2025)
por: Aditya, Ramit, et al.
Publicado: (2025)
Causality-Inspired Fair Representation Learning for Multimodal Recommendation
por: Chen, Weixin, et al.
Publicado: (2023)
por: Chen, Weixin, et al.
Publicado: (2023)
The Double-Edged Sword of Knowledge Transfer: Diagnosing and Curing Fairness Pathologies in Cross-Domain Recommendation
por: Zhao, Yuhan, et al.
Publicado: (2026)
por: Zhao, Yuhan, et al.
Publicado: (2026)
Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data
por: Zhao, Yuhan, et al.
Publicado: (2024)
por: Zhao, Yuhan, et al.
Publicado: (2024)
ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender Systems
por: Zhang, Yi, et al.
Publicado: (2026)
por: Zhang, Yi, et al.
Publicado: (2026)
Exploring Recommender System Evaluation: A Multi-Modal User Agent Framework for A/B Testing
por: Zhang, Wenlin, et al.
Publicado: (2026)
por: Zhang, Wenlin, et al.
Publicado: (2026)
MemRec: Collaborative Memory-Augmented Agentic Recommender System
por: Chen, Weixin, et al.
Publicado: (2026)
por: Chen, Weixin, et al.
Publicado: (2026)
Collaborative Knowledge Fusion: A Novel Approach for Multi-task Recommender Systems via LLMs
por: Zhao, Chuang, et al.
Publicado: (2024)
por: Zhao, Chuang, et al.
Publicado: (2024)
Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis
por: Xu, Lanling, et al.
Publicado: (2024)
por: Xu, Lanling, et al.
Publicado: (2024)
Dissertation: On the Theoretical Foundation of Model Comparison and Evaluation for Recommender System
por: Li, Dong
Publicado: (2024)
por: Li, Dong
Publicado: (2024)
Divergence Meets Consensus: A Multi-Source Negative Sampling Framework for Sequential Recommendation
por: Li, Yuanzi, et al.
Publicado: (2026)
por: Li, Yuanzi, et al.
Publicado: (2026)
Limitations of Current Evaluation Practices for Conversational Recommender Systems and the Potential of User Simulation
por: Bernard, Nolwenn, et al.
Publicado: (2025)
por: Bernard, Nolwenn, et al.
Publicado: (2025)
Unleashing the Potential of Neighbors: Diffusion-based Latent Neighbor Generation for Session-based Recommendation
por: Yang, Yuhan, et al.
Publicado: (2026)
por: Yang, Yuhan, et al.
Publicado: (2026)
Sparser Training for On-Device Recommendation Systems
por: Qu, Yunke, et al.
Publicado: (2024)
por: Qu, Yunke, et al.
Publicado: (2024)
Recommender Systems in the Era of Large Language Models (LLMs)
por: Zhao, Zihuai, et al.
Publicado: (2023)
por: Zhao, Zihuai, et al.
Publicado: (2023)
Deep Research for Recommender Systems
por: Ou, Kesha, et al.
Publicado: (2026)
por: Ou, Kesha, et al.
Publicado: (2026)
Enhancing Transferability and Consistency in Cross-Domain Recommendations via Supervised Disentanglement
por: Wang, Yuhan, et al.
Publicado: (2025)
por: Wang, Yuhan, et al.
Publicado: (2025)
Preliminary Evaluation of the Test-Time Training Layers in Recommendation System (Student Abstract)
por: Zhan, Tianyu, et al.
Publicado: (2024)
por: Zhan, Tianyu, et al.
Publicado: (2024)
Sustainability Evaluation Metrics for Recommender Systems
por: Felfernig, Alexander, et al.
Publicado: (2025)
por: Felfernig, Alexander, et al.
Publicado: (2025)
Generative Reasoning Recommendation via LLMs
por: Hong, Minjie, et al.
Publicado: (2025)
por: Hong, Minjie, et al.
Publicado: (2025)
Towards Next-Generation Recommender Systems: A Benchmark for Personalized Recommendation Assistant with LLMs
por: Huang, Jiani, et al.
Publicado: (2025)
por: Huang, Jiani, et al.
Publicado: (2025)
Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers
por: Zhang, Zhiyang, et al.
Publicado: (2026)
por: Zhang, Zhiyang, et al.
Publicado: (2026)
Continual Collaborative Distillation for Recommender System
por: Lee, Gyuseok, et al.
Publicado: (2024)
por: Lee, Gyuseok, et al.
Publicado: (2024)
KGBridge: Knowledge-Guided Prompt Learning for Non-overlapping Cross-Domain Recommendation
por: Wang, Yuhan, et al.
Publicado: (2025)
por: Wang, Yuhan, et al.
Publicado: (2025)
Decision-aware User Simulation Agent for Evaluating Conversational Recommender Systems
por: Li, Yuan-Chi, et al.
Publicado: (2026)
por: Li, Yuan-Chi, et al.
Publicado: (2026)
Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders
por: Hou, Yupeng, et al.
Publicado: (2024)
por: Hou, Yupeng, et al.
Publicado: (2024)
Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
por: Wang, Hao, et al.
Publicado: (2025)
por: Wang, Hao, et al.
Publicado: (2025)
The Potential of AutoML for Recommender Systems
por: Vente, Tobias, et al.
Publicado: (2024)
por: Vente, Tobias, et al.
Publicado: (2024)
Exploring Preference-Guided Diffusion Model for Cross-Domain Recommendation
por: Li, Xiaodong, et al.
Publicado: (2025)
por: Li, Xiaodong, et al.
Publicado: (2025)
Improving Multi-modal Recommender Systems by Denoising and Aligning Multi-modal Content and User Feedback
por: Xv, Guipeng, et al.
Publicado: (2024)
por: Xv, Guipeng, et al.
Publicado: (2024)
Leveraging LLMs for Influence Path Planning in Proactive Recommendation
por: Wang, Mingze, et al.
Publicado: (2024)
por: Wang, Mingze, et al.
Publicado: (2024)
The Hidden Cost of Defaults in Recommender System Evaluation
por: Berling, Hannah, et al.
Publicado: (2025)
por: Berling, Hannah, et al.
Publicado: (2025)
Offline Evaluation Measures of Fairness in Recommender Systems
por: Rampisela, Theresia Veronika
Publicado: (2026)
por: Rampisela, Theresia Veronika
Publicado: (2026)
Continual Recommender Systems
por: Yoo, Hyunsik, et al.
Publicado: (2025)
por: Yoo, Hyunsik, et al.
Publicado: (2025)
Distributionally Robust Graph-based Recommendation System
por: Wang, Bohao, et al.
Publicado: (2024)
por: Wang, Bohao, et al.
Publicado: (2024)
Ejemplares similares
-
Can Large Language Models Assess Serendipity in Recommender Systems?
por: Tokutake, Yu, et al.
Publicado: (2024) -
A Universal Framework for Offline Serendipity Evaluation in Recommender Systems via Large Language Models
por: Tokutake, Yu, et al.
Publicado: (2025) -
Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
por: Xi, Yunjia, et al.
Publicado: (2025) -
Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models
por: Yong, Qian, et al.
Publicado: (2025) -
Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users
por: Chen, Weixin, et al.
Publicado: (2025)