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
this https URL

我们提出了 PAP-REC，一种用于生成个性化自动提示的 RECommendation 语言模型的框架，旨在解决手动设计提示的低效和低效问题，并通过实验证明自动生成的提示优于手动构造的提示和各种基线推荐模型。