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jeykigung · Aug 1, 2022 · 04e0b10 · 04e0b10
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+# Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)
+
+> Paper link: https://arxiv.org/pdf/2203.13366.pdf
+
+![Teaser](pic/teaser.png)
+
+## Introduction
+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. Specifically, 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 universal 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.
+
+## Requirements:
+- Python 3.9.7
+- PyTorch 1.10.1
+- transformers 4.2.1
+- tqdm
+- numpy
+- sentencepiece
+- pyyaml
+
+
+## Usage
+
+0. Clone this repo
+
+    ```
+    git clone https://github.com/jeykigung/P5.git
+    ```
+
+1. Download preprocessed data from this [Google Drive link](https://drive.google.com/file/d/1qGxgmx7G_WB7JE4Cn_bEcZ_o_NAJLE3G/view?usp=sharing), then put them into the *data* folder. If you would like to preprocess your own data, please follow the jupyter notebooks in the *preprocess* folder. Raw data can be downloaded from this [Google Drive link](https://drive.google.com/file/d/1uE-_wpGmIiRLxaIy8wItMspOf5xRNF2O/view?usp=sharing), then put them into the *raw_data* folder.
+
+   
+2. Download pretrained checkpoints into *snap* folder. If you would like to train your own P5 models, *snap* folder will also be used to store P5 checkpoints.
+
+
+3. Pretrain with scripts in *scripts* folder, such as
+
+    ```
+    bash scripts/pretrain_P5_base_beauty.sh 4
+    ```
+    
+4. Evaluate with example jupyter notebooks in the *notebooks* folder. Before testing, create a soft link of *data* folder to the *notebooks* folder by
+   
+   ```
+   cd notebooks
+   ln -s ../data .
+   ```
+
+
+## Pretrained Checkpoints
+See [CHECKPOINTS.md](snap/CHECKPOINTS.md).
+
+
+## Citation
+
+Please cite the following paper corresponding to the repository:
+```
+@inproceedings{geng2022recommendation,
+  title={Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt \& Predict Paradigm (P5)},
+  author={Geng, Shijie and Liu, Shuchang and Fu, Zuohui and Ge, Yingqiang and Zhang, Yongfeng},
+  booktitle={Sixteenth ACM Conference on Recommender Systems},
+  year={2022}
+}
+```
+
+## Acknowledgements
+
+[VL-T5](https://github.com/j-min/VL-T5), [PETER](https://github.com/lileipisces/PETER), and [S3-Rec](https://github.com/aHuiWang/CIKM2020-S3Rec)