Abstract
For a long period, different recommendation tasks typically require designing task-specific architectures and training objectives. As a result, it is hard to transfer the learned knowledge and representations from one task to another, thus restricting the generalization ability of existing recommendation approaches, e.g., a sequential recommendation model can hardly be applied or transferred to a review generation method. To deal with such issues, considering that language grounding is a powerful medium to describe and represent various problems or tasks, we present a flexible and unified text-to-text paradigm called "Pretrain, Personalized Prompt, and Predict Paradigm" (P5) for recommendation, which unifies various recommendation tasks in a shared framework. In P5, all data such as user-item interactions, item metadata, and user reviews are converted to a common format -- natural language sequences. The rich information from natural language assist P5 to capture deeper semantics for recommendation. P5 learns different tasks with the same language modeling objective during pretraining. Thus, it possesses the potential to serve as the foundation model for downstream recommendation tasks, allows easy integration with other modalities, and enables instruction-based recommendation, which will revolutionize the technical form of recommender system towards unified recommendation engine. With adaptive personalized prompt for different users, P5 is able to make predictions in a zero-shot or few-shot manner and largely reduces the necessity for extensive fine-tuning. On several recommendation benchmarks, we conduct experiments to show the effectiveness of our generative approach. We will release our prompts and pretrained P5 language model to help advance future research on Recommendation as Language Processing (RLP) and Personalized Foundation Models.
Abstract (translated)
URL
https://arxiv.org/abs/2203.13366
