Recommender systems are widely used to suggest engaging content, and Large Language Models (LLMs) have given rise to generative recommenders. Such systems can directly generate items, including for open-set tasks like question suggestion. While the world knowledge of LLMs enable good recommendations, improving the generated content through user feedback is challenging as continuously fine-tuning LLMs is prohibitively expensive. We present a training-free approach for optimizing generative recommenders by connecting user feedback loops to LLM-based optimizers. We propose a generative explore-exploit method that can not only exploit generated items with known high engagement, but also actively explore and discover hidden population preferences to improve recommendation quality. We evaluate our approach on question generation in two domains (e-commerce and general knowledge), and model user feedback with Click Through Rate (CTR). Experiments show our LLM-based explore-exploit approach can iteratively improve recommendations, and consistently increase CTR. Ablation analysis shows that generative exploration is key to learning user preferences, avoiding the pitfalls of greedy exploit-only approaches. A human evaluation strongly supports our quantitative findings.

通过将用户反馈环路与基于大型语言模型的优化器相连接，我们提出了一种无需训练的优化生成式推荐器的方法，该方法不仅能够利用已知高度参与的生成内容，还能主动探索和发现隐藏的人群偏好以提高推荐质量。我们在两个领域（电子商务和一般知识）上评估了我们的方法，并使用点击率（CTR）对用户反馈进行建模。实验表明，我们的基于大型语言模型的探索-开发方法可以迭代地改进推荐，且不断提高CTR。消融分析表明，生成式探索是学习用户偏好的关键，避免了贪婪的仅开发方法的陷阱。人类评估强烈支持我们的定量发现。

生成型探索利用：基于LLM优化器的无需训练优化生成型推荐系统