Papers by Gian Maria Marconi
AlignUSER: Human-Aligned LLM Agents via World Models for Recommender System Evaluation (2026.acl-long)
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| Challenge: | Existing evaluation practices for recommender systems rely on few-shot prompting and offline metrics are often misaligned with online behavior. |
| Approach: | They propose a framework that learns world-model-driven agents from human interactions. |
| Outcome: | The proposed framework enables agents to express rich preferences and feedback in natural language and interact with recommender systems in a simulation. |