Recently emerged prompt-based Recommendation Language Models (RLM) can solve multiple recommendation tasks uniformly. The RLMs make full use of the inherited knowledge learned from the abundant pre-training data to solve the downstream recommendation tasks by prompts, without introducing additional parameters or network training. However, handcrafted prompts require significant expertise and human effort since slightly rewriting prompts may cause massive performance changes. In this paper, we propose PAP-REC, a framework to generate the Personalized Automatic Prompt for RECommendation language models to mitigate the inefficiency and ineffectiveness problems derived from manually designed prompts. Specifically, personalized automatic prompts allow different users to have different prompt tokens for the same task, automatically generated using a gradient-based method. One challenge for personalized automatic prompt generation for recommendation language models is the extremely large search space, leading to a long convergence time. To effectively and efficiently address the problem, we develop surrogate metrics and leverage an alternative updating schedule for prompting recommendation language models. Experimental results show that our PAP-REC framework manages to generate personalized prompts, and the automatically generated prompts outperform manually constructed prompts and also outperform various baseline recommendation models. The source code of the work is available at https://github.com/rutgerswiselab/PAP-REC.

翻译：近期涌现的基于提示的推荐语言模型（RLM）能够统一解决多种推荐任务。RLM充分利用从丰富的预训练数据中习得的继承知识，通过提示解决下游推荐任务，无需引入额外参数或网络训练。然而，人工设计的提示需要大量专业知识和人力投入，因为对提示的轻微修改可能导致性能大幅波动。本文提出PAP-REC框架，用于生成面向推荐语言模型的个性化自动提示，以缓解人工设计提示带来的低效与无效问题。具体而言，个性化自动提示允许不同用户针对相同任务拥有不同的提示令牌，这些令牌通过基于梯度的方法自动生成。推荐语言模型的个性化自动提示生成面临的一个挑战是搜索空间极大，从而导致收敛时间较长。为有效且高效地解决该问题，我们开发了替代指标并采用交替更新策略来提示推荐语言模型。实验结果表明，PAP-REC框架能够生成个性化提示，且自动生成的提示性能优于人工构建的提示以及多种基线推荐模型。该工作的源代码可在https://github.com/rutgerswiselab/PAP-REC获取。