Xinyu Lin
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The paper proposes a new method, AIMS, to improve instruction-guided generative recommendation by converting the effect of removing individual history events into ranking supervision.
This paper introduces AutoRSE, an evaluation framework for Recommender Systems Engineer Agents (RSE-Agents) and conducts a large-scale study to understand their capabilities and limitations.
Papers
AutoRSE: Evaluating LLM Agents as Recommendation Engineers for Self-Evolving Recommender Systems
This paper introduces AutoRSE, an evaluation framework for Recommender Systems Engineer Agents (RSE-Agents) and conducts a large-scale study to understand their capabilities and limitations.