EasyRec-Large

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 within English.

For details please refer to our [💻GitHub Code] and [📖Paper].

Get Started

Environment

Please run the following commands to create a conda environment:

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.

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.

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])    # 0.8576
print(embeddings[0] @ embeddings[2])    # 0.2171

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	82M	0.0286
hkuds/easyrec-roberta-base	125M	0.0518
hkuds/easyrec-roberta-large	355M	0.0557

🌟 Citation

If you find this work is helpful to your research, please consider citing our paper:

@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!