# Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)

**Authors:** Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, Yongfeng Zhang  
**arXiv:** [2203.13366](https://arxiv.org/abs/2203.13366)  
**Date:** 2022-03-24  
**Summary page:** https://www.summarizepaper.com/en/paper/2203.13366v1/

## Key points

- Recommendation systems struggle with designing task-specific architectures and training objectives for different recommendation tasks
- This makes it difficult to transfer learned knowledge and representations from one task to another
- A team of researchers has proposed a flexible and unified text-to-text paradigm called "Pretrain, Personalized Prompt, and Predict Paradigm" (P5) for recommendation
- P5 unifies various recommendation tasks in a shared framework by converting all data into natural language sequences
- During pretraining, P5 learns different tasks with the same language modeling objective which makes it a potential foundation model for downstream recommendation tasks that allows easy integration with other modalities and enables instruction-based recommendation
- One key feature of P5 is its adaptive personalized prompt for different users which allows it to make predictions in a zero-shot or few-shot manner and largely reduces the necessity for extensive fine-tuning
- The authors conducted experiments on several recommendation benchmarks to demonstrate the effectiveness of their generative approach
- They plan to release their prompts and pretrained P5 language model to advance future research on Recommendation as Language Processing (RLP) and Personalized Foundation Models.

## Summary

The field of recommendation systems has long struggled with the challenge of designing task-specific architectures and training objectives for different recommendation tasks, making it difficult to transfer learned knowledge and representations from one task to another. To address this issue, a team of researchers has proposed a flexible and unified text-to-text paradigm called "Pretrain, Personalized Prompt, and Predict Paradigm" (P5) for recommendation. This approach unifies various recommendation tasks in a shared framework by converting all data such as user-item interactions, item metadata, and user reviews into natural language sequences. The rich information from natural language helps P5 capture deeper semantics for recommendation. During pretraining, P5 learns different tasks with the same language modeling objective which makes it a potential foundation model for downstream recommendation tasks that allows easy integration with other modalities and enables instruction-based recommendation. One key feature of P5 is its adaptive personalized prompt for different users which allows it to make predictions in a zero-shot or few-shot manner and largely reduces the necessity for extensive fine-tuning. The authors conducted experiments on several recommendation benchmarks to demonstrate the effectiveness of their generative approach. They plan to release their prompts and pretrained P5 language model to advance future research on Recommendation as Language Processing (RLP) and Personalized Foundation Models. Overall, this paper presents an innovative solution that could revolutionize the technical form of recommender systems towards a unified recommendation engine by leveraging language grounding as a powerful medium to describe and represent various problems or tasks.