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[WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"
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| 2026-08-03 | 537 |
| 2026-08-06 | 537 |
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# LLMRec: Large Language Models with Graph Augmentation for Recommendation
<img src='image/LLMRec.png' />
PyTorch implementation for WSDM 2024 paper [LLMRec: Large Language Models with Graph Augmentation for Recommendation](https://arxiv.org/pdf/2311.00423.pdf).
[Wei Wei](#), [Xubin Ren](https://rxubin.com/), [Jiabin Tang](https://tjb-tech.github.io/), [Qingyong Wang](#), [Lixin Su](#), [Suqi Cheng](#), [Junfeng Wang](#), [Dawei Yin](https://www.yindawei.com/) and [Chao Huang](https://sites.google.com/view/chaoh/home)*.
(*Correspondence)
**[Data Intelligence Lab](https://sites.google.com/view/chaoh/home)@[University of Hong Kong](https://www.hku.hk/)**, Baidu Inc.
<a href='https://llmrec.github.io/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
<a href='https://llmrec.github.io/'><img src='https://img.shields.io/badge/Demo-Page-purple'></a>
<a href='https://arxiv.org/pdf/2311.00423.pdf'><img src='https://img.shields.io/badge/Paper-PDF-orange'></a>
[](https://www.youtube.com/channel/UC1wKlPPlP9zKGYk62yR0K_g)
This repository hosts the code, original data and augmented data of **LLMRec**.
-----------
<p align="center">
<img src="./image/llmrec_framework.png" alt="LLMRec" />
</p>
LLMRec is a novel framework that enhances recommenders by applying three simple yet effective LLM-based graph augmentation strategies to recommendation system. LLMRec is to make the most of the content within online platforms (e.g., Netflix, MovieLens) to augment interaction graph by i) reinforcing u-i interactive edges, ii) enhancing item node attributes, and iii) conducting user node profiling, intuitively from the natural language perspective.
-----------
## 🎉 News 📢📢
- [x] [2024.3.20] 🚀🚀 📢📢📢📢🌹🔥🔥🚀🚀 Because baselines `LATTICE` and `MMSSL` require some minor modifications, we provide code that can be easily run by simply modifying the dataset path.
- [x] [2023.11.3] 🚀🚀 Release the script for constructing the prompt.
- [x] [2023.11.1] 🔥🔥 Release the multi-modal datasets (Netflix, MovieLens), including textual data and visual data.
- [x] [2023.11.1] 🚀🚀 Release LLM-augmented textual data(by gpt-3.5-turbo-0613), and LLM-augmented embedding(by text-embedding-ada-002).
- [x] [2023.10.28] 🔥🔥 The full paper of our LLMRec is available at [LLMRec: Large Language Models with Graph Augmentation for Recommendation](https://arxiv.org/pdf/2311.00423.pdf).
- [x] [2023.10.28] 🚀🚀 Release the code of LLMRec.
## 👉 TODO
- [ ] Provide different larger version of the datasets.
- [ ] ...
-----------
<h2> Dependencies </h2>
```
pip install -r requirements.txt
```
<h2>Usage </h2>
<h4>Stage 1: LLM-based Data Augmentation</h4>
```
cd LLMRec/LLM_augmentation/
python ./gpt_ui_aug.py
python ./gpt_user_profiling.py
python ./gpt_i_attribute_generate_aug.py
```
<h4>Stage 2: Recommender training with LLM-augmented Data</h4>
```
cd LLMRec/
python ./main.py --dataset {DATASET}
```
Supported datasets: `netflix`, `movielens`
Specific code execution example on 'netflix':
```
# LLMRec
python ./main.py
# w/o-u-i
python ./main.py --aug_sample_rate=0.0
# w/o-u
python ./main.py --user_cat_rate=0
# w/o-u&i
python ./main.py --user_cat_rate=0 --item_cat_rate=0
# w/o-prune
python ./main.py --prune_loss_drop_rate=0
```
-----------
<h2> Datasets </h2>
```
├─ LLMRec/
├── data/
├── netflix/
...
```
<h3> Multi-modal Datasets </h3>
🌹🌹 Please cite our paper if you use the 'netflix' dataset~ ❤️
We collected a multi-modal dataset using the original [Netflix Prize Data](https://www.kaggle.com/datasets/netflix-inc/netflix-prize-data) released on the [Kaggle](https://www.kaggle.com/) website. The data format is directly compatible with state-of-the-art multi-modal recommendation models like [LLMRec](https://github.com/HKUDS/LLMRec), [MMSSL](https://github.com/HKUDS/MMSSL), [LATTICE](https://github.com/CRIPAC-DIG/LATTICE), [MICRO](Excerpt of 11,532 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:07b1de824668672e, llm:Repository topics: colloborative-filtering, content-based-recommendation, data-augmentation-strategies, graph-augmentation, graph-learning, multi-modal-recommendation, recommendation-system, recommendation-with-side-information; description: 'LLMRec: Large Language Models with Graph Augmentation for Recommendation' (WSDM 2024).
matched fp:07b1de824668672e, llm:Repository topics: colloborative-filtering, content-based-recommendation, data-augmentation-strategies, graph-augmentation, graph-learning, multi-modal-recommendation, recommendation-system, recommendation-with-side-information; description: 'LLMRec: Large Language Models with Graph Augmentation for Recommendation' (WSDM 2024).
matched fp:07b1de824668672e, llm:Repository topics: colloborative-filtering, content-based-recommendation, data-augmentation-strategies, graph-augmentation, graph-learning, multi-modal-recommendation, recommendation-system, recommendation-with-side-information; description: 'LLMRec: Large Language Models with Graph Augmentation for Recommendation' (WSDM 2024).