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---
license: apache-2.0
language:
- en
tags:
- recommendation
- collaborative filtering
---
# EasyRec-Base
## Overview
- **Description**: EasyRec is a series of language models designed for recommendations, trained to match the textual profiles of users and items with collaborative signals.
- **Usage**: You can use EasyRec to encode user and item text embeddings based on the textual profiles that reflect their preferences for various recommendation scenarios.
- **Evaluation**: We evaluate the performance of EasyRec in: (i) Text-based zero-shot recommendation and (ii) Text-enhanced collaborative filtering.
- **Finetuned from model:** EasyRec is finetuned from [RoBERTa](https://huggingface.co/FacebookAI/roberta-large) within English.
For details please refer to our [💻[GitHub Code](https://github.com/HKUDS/EasyRec)] and [📖[Paper]()].
## Get Started
### Environment
Please run the following commands to create a conda environment:
```bash
conda create -y -n easyrec python=3.11
pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1
pip install -U "transformers==4.40.0" --upgrade
pip install accelerate==0.28.0
pip install tqdm
pip install sentencepiece==0.2.0
pip install scipy==1.9.3
pip install setproctitle
pip install sentence_transformers
```
### Example Codes
Please first download the codes.
```ssh
git clone https://github.com/HKUDS/EasyRec.git
cd EasyRec
```
Here is an example code snippet to utilize EasyRec for encoding **text embeddings** based on user and item profiles for recommendations.
```Python
import torch
from model import Easyrec
import torch.nn.functional as F
from transformers import AutoConfig, AutoModel, AutoTokenizer
config = AutoConfig.from_pretrained("hkuds/easyrec-roberta-large")
model = Easyrec.from_pretrained("hkuds/easyrec-roberta-large", config=config,)
tokenizer = AutoTokenizer.from_pretrained("hkuds/easyrec-roberta-large", use_fast=False,)
profiles = [
'This user is a basketball fan and likes to play basketball and watch NBA games.', # user
'This basketball draws in NBA enthusiasts.', # item 1
'This item is nice for swimming lovers.' # item 2
]
inputs = tokenizer(profiles, padding=True, truncation=True, max_length=512, return_tensors="pt")
with torch.inference_mode():
embeddings = model.encode(input_ids=inputs.input_ids, attention_mask=inputs.attention_mask)
embeddings = F.normalize(embeddings.pooler_output.detach().float(), dim=-1)
print(embeddings[0] @ embeddings[1]) # 0.8971
print(embeddings[0] @ embeddings[2]) # 0.2904
```
### Model List
We release a series of EasyRec checkpoints with varying sizes. You can easily load these models from Hugging Face by replacing the model name.
| Model | Model Size | Recall@20 on Amazon-Sports |
|:-------------------------------|:--------:| :--------:|
| [hkuds/easyrec-roberta-small](https://huggingface.co/hkuds/easyrec-roberta-small) | 82M | 0.0286 |
| [hkuds/easyrec-roberta-base](https://huggingface.co/hkuds/easyrec-roberta-base) | 125M | 0.0518 |
| [hkuds/easyrec-roberta-large](https://huggingface.co/hkuds/easyrec-roberta-large) | 355M | 0.0557 |
## 🌟 Citation
If you find this work is helpful to your research, please consider citing our paper:
```bibtex
@article{ren2024easyrec,
title={EasyRec: Simple yet Effective Language Models for Recommendation},
author={Ren, Xubin and Huang, Chao},
journal={arXiv preprint arXiv:2408.08821},
year={2024}
}
```
**Thanks for your interest in our work!** |