Pre-training models have shown their power in sequential recommendation. Recently, prompt has been widely explored and verified for tuning in NLP pre-training, which could help to more effectively and efficiently extract useful knowledge from pre-training models for downstream tasks, especially in cold-start scenarios. However, it is challenging to bring prompt-tuning from NLP to recommendation, since the tokens in recommendation (i.e., items) do not have explicit explainable semantics, and the sequence modeling should be personalized. In this work, we first introduces prompt to recommendation and propose a novel Personalized prompt-based recommendation (PPR) framework for cold-start recommendation. Specifically, we build the personalized soft prefix prompt via a prompt generator based on user profiles and enable a sufficient training of prompts via a prompt-oriented contrastive learning with both prompt- and behavior-based augmentations. We conduct extensive evaluations on various tasks. In both few-shot and zero-shot recommendation, PPR models achieve significant improvements over baselines on various metrics in three large-scale open datasets. We also conduct ablation tests and sparsity analysis for a better understanding of PPR. Moreover, We further verify PPR's universality on different pre-training models, and conduct explorations on PPR's other promising downstream tasks including cross-domain recommendation and user profile prediction.

翻译：预训练模型已在序列推荐中展现出强大能力。近期，提示学习在自然语言处理预训练中得到了广泛探索与验证，它能够更高效地提取预训练模型中的有用知识以服务于下游任务，尤其在冷启动场景下表现突出。然而，将提示学习从自然语言处理迁移至推荐领域面临挑战：推荐系统中的令牌（即物品）不具备显式可解释语义，且序列建模需实现个性化。本研究首次将提示学习引入推荐系统，提出基于个性化提示的推荐框架（PPR）以解决冷启动推荐问题。具体而言，我们通过基于用户画像的提示生成器构建个性化软前缀提示，并利用面向提示的对比学习（融合提示增强与行为增强）实现提示的充分训练。我们在不同任务上开展了广泛评估。在少样本与零样本推荐场景下，PPR模型在三个大规模公开数据集的多项指标上均显著优于基线模型。通过消融实验与稀疏性分析进一步揭示了PPR的内在机理。此外，我们验证了PPR在不同预训练模型上的通用性，并探索了其在跨域推荐与用户画像预测等潜在下游任务中的表现。