Amazon datasets

The 'Amazon datasets' consist of user-item interaction data used to evaluate the effectiveness of recommendation systems, particularly in the context of leveraging large language models for improved ranking and utility optimization.

Papers using Amazon datasets (9)

LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation2026AMEM4Rec: Leveraging Cross-User Similarity for Memory Evolution in Agentic LLM Recommenders2026Reasoning-guided Collaborative Filtering with Language Models for Explainable Recommendation2026Reasoning to Rank: An End-to-End Solution for Exploiting Large Language Models for Recommendation2026ExplainRec: Towards Explainable Multi-Modal Zero-Shot Recommendation with Preference Attribution and Large Language Models2025Iterative Critique-Refine Framework for Enhancing LLM Personalization2025X-Cross: Dynamic Integration of Language Models for Cross-Domain Sequential Recommendation2025LLM-based User Profile Management for Recommender System2025LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering2024