Copertina

Collaborative User Prompt for Personalized Generative Recommendation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ramos, Jerome, Wu, Bin, Lipani, Aldo
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Information Retrieval
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Transparent and Scrutable Recommendations Using Natural Language User Profiles
di: Ramos, Jerome, et al.
Pubblicazione: (2024)

Interplay: Training Independent Simulators for Reference-Free Conversational Recommendation
di: Ramos, Jerome, et al.
Pubblicazione: (2026)

A Reproducibility and Generalizability Study of Large Language Models for Query Generation
di: Staudinger, Moritz, et al.
Pubblicazione: (2024)

Personalized Prompt for Sequential Recommendation
di: Wu, Yiqing, et al.
Pubblicazione: (2022)

Reinforced Prompt Personalization for Recommendation with Large Language Models
di: Mao, Wenyu, et al.
Pubblicazione: (2024)

Automating Personalization: Prompt Optimization for Recommendation Reranking
di: Wang, Chen, et al.
Pubblicazione: (2025)

PCL: Prompt-based Continual Learning for User Modeling in Recommender Systems
di: Yang, Mingdai, et al.
Pubblicazione: (2025)

Post-Training Denoising of User Profiles with LLMs in Collaborative Filtering Recommendation
di: Dervishaj, Ervin, et al.
Pubblicazione: (2026)

Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation
di: Qu, Liang, et al.
Pubblicazione: (2024)

Utilizing Collaborative Filtering in a Personalized Research-Paper Recommendation System
di: Hasan, Mahamudul, et al.
Pubblicazione: (2024)

Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems
di: Shi, Chenxi, et al.
Pubblicazione: (2024)

UQABench: Evaluating User Embedding for Prompting LLMs in Personalized Question Answering
di: Liu, Langming, et al.
Pubblicazione: (2025)

Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation
di: Kusano, Genki, et al.
Pubblicazione: (2025)

Short Video Segment-level User Dynamic Interests Modeling in Personalized Recommendation
di: He, Zhiyu, et al.
Pubblicazione: (2025)

Personalized Multi-Interest Modeling for Cross-Domain Recommendation to Cold-Start Users
di: Li, Xiaodong, et al.
Pubblicazione: (2026)

Content-Based Collaborative Generation for Recommender Systems
di: Wang, Yidan, et al.
Pubblicazione: (2024)

CEMG: Collaborative-Enhanced Multimodal Generative Recommendation
di: Lin, Yuzhen, et al.
Pubblicazione: (2025)

S-GRec: Personalized Semantic-Aware Generative Recommendation with Asymmetric Advantage
di: Jiang, Jie, et al.
Pubblicazione: (2026)

Towards Robust Cross-Domain Recommendation with Joint Identifiability of User Preference
di: Du, Jing, et al.
Pubblicazione: (2024)

Selecting User Histories to Generate LLM Users for Cold-Start Item Recommendation
di: Subbaraman, Nachiket, et al.
Pubblicazione: (2025)

Accelerating Generative Recommendation via Simple Categorical User Sequence Compression
di: Liu, Qijiong, et al.
Pubblicazione: (2026)

Device-Cloud Collaborative Correction for On-Device Recommendation
di: Zhan, Tianyu, et al.
Pubblicazione: (2025)

Multimodal Generative Recommendation for Fusing Semantic and Collaborative Signals
di: Vandenhirtz, Moritz, et al.
Pubblicazione: (2026)

Collaborative Large Language Model for Recommender Systems
di: Zhu, Yaochen, et al.
Pubblicazione: (2023)

PIT: A Dynamic Personalized Item Tokenizer for End-to-End Generative Recommendation
di: Wang, Huanjie, et al.
Pubblicazione: (2026)

EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration
di: Wang, Ye, et al.
Pubblicazione: (2024)

Token-level Collaborative Alignment for LLM-based Generative Recommendation
di: Lin, Fake, et al.
Pubblicazione: (2026)

DiscRec: Disentangled Semantic-Collaborative Modeling for Generative Recommendation
di: Liu, Chang, et al.
Pubblicazione: (2025)

Next-User Retrieval: Enhancing Cold-Start Recommendations via Generative Next-User Modeling
di: Lan, Yu-Ting, et al.
Pubblicazione: (2025)

LWGR: Lagrangian-Constrained Personalized World Knowledge for Generative Recommendation
di: Mu, Lingyu, et al.
Pubblicazione: (2026)

Multi-Agent Collaborative Filtering: Orchestrating Users and Items for Agentic Recommendations
di: Xia, Yu, et al.
Pubblicazione: (2025)

Understanding and Improving Adversarial Collaborative Filtering for Robust Recommendation
di: Zhang, Kaike, et al.
Pubblicazione: (2024)

Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendations
di: Islam, Md Aminul, et al.
Pubblicazione: (2025)

Efficient Sequential Recommendation for Long Term User Interest Via Personalization
di: Zhang, Qiang, et al.
Pubblicazione: (2026)

Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling
di: Li, Qiang, et al.
Pubblicazione: (2025)

Awakening Dormant Users: Generative Recommendation with Counterfactual Functional Role Reasoning
di: Luo, Huishi, et al.
Pubblicazione: (2026)

Towards Distribution Matching between Collaborative and Language Spaces for Generative Recommendation
di: Zhang, Yi, et al.
Pubblicazione: (2025)

Personalized Denoising Implicit Feedback for Robust Recommender System
di: Zhang, Kaike, et al.
Pubblicazione: (2025)

Top-Personalized-K Recommendation
di: Kweon, Wonbin, et al.
Pubblicazione: (2024)

Debiasing Recommendation with Personal Popularity
di: Ning, Wentao, et al.
Pubblicazione: (2024)