EasyRec: Simple yet Effective Language Model for Recommendation

This is the PyTorch implementation by @Re-bin for EasyRec model proposed in this paper:

EasyRec: Simple yet Effective Language Models for Recommendation
Xubin Ren, Chao Huang*

* denotes corresponding author

A Movie Recommendation App is deployed on Huggingface

In this paper, we propose an effective language model, EasyRec, for recommendation. EasyRec is trained using collaborative information from multiple recommendation datasets, leveraging collaborative user/item profiles as input and employing novel contrastive learning objectives. By encoding user/item profiles into high-quality semantic embeddings suitable for recommendation, EasyRec demonstrates strong performance in text-based zero-shot recommendations and text-enhanced collaborative filtering scenarios.

📝 Environment

Please run the following commands to create a conda environment:

conda create -y -n easyrec python=3.11
conda activate easyrec
pip install -r requirements.txt

🚀 Use EasyRec

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.

sample.py

import torch
from model import Easyrec
import torch.nn.functional as F
from transformers import AutoConfig, AutoModel, AutoTokenizer

hf_model = "jibala-1022/easyrec-base"
config = AutoConfig.from_pretrained(hf_model)
model = Easyrec.from_pretrained(hf_model, config=config,)
tokenizer = AutoTokenizer.from_pretrained(hf_model, 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.

HF 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

Local Model	Model Size	Recall@10 on Movies
jibala-1022/easyrec-small	243M	0.0086
jibala-1022/easyrec-base	328M	0.0166

📚 Datasets with User/Item Profiles (for training and evaluation)

You can download the data by running following commands.

wget https://archive.org/download/easyrec_data/data.zip
unzip data.zip

You can also download our data from the [Google Drive]

We utilize six datasets for training (arts, sports, games, home, electronics, tools) and one dataset for testing (movies). The steam and yelp datasets are processed in accordance with previous work (RLMRec), while the others are derived from the Amazon Review Data v2.

For the training datasets, the files in the folder follow this structure:

- arts/electronics/games/home/movies/tools:
|--- diverse_profile    # Diversified user/item profiles
|--- prompts            # Prompts to obtain the user/item_profile.json
|--- user_profile.json  # User profiles
|--- item_profile.json  # Item profiles
|--- trn_mat.pkl        # Training Interactions
|--- val_mat.pkl        # Validation Interactions
|--- tst_mat.pkl        # Test Interactions (NOT USED)

For the testing datasets, the files in the folder follow this structure:

- sports/steam/yelp:
|--- diverse_profile    # Diversified user/item profiles
|--- prompts            # Prompts to obtain the user/item_profile.json (only sports)
|--- user_profile.json  # User profiles
|--- item_profile.json  # Item profiles
|--- trn_mat.pkl        # Training Interactions
|--- val_mat.pkl        # Validation Interactions (NOT USED in text-based recommendation)
|--- tst_mat.pkl        # Test Interactions

🤗 In the prompts folder (only for the datasets we process here), we profile the input prompt for large language models to obtain the collaborative profiles of users and items. The JSON files also contain the original ID (e.g., ASIN) of users/items in the original dataset. For details of the steam and yelp datasets, please kindly refer to RLMRec.

🚀 Training

EasyRec follows the same model achitecture of RoBERTa.

Install Git-LFS to handle large Huggingface models:

sudo apt install git-lfs
git lfs install

First of all, please download the checkpoint of RoBERTa with the following commands:

mkdir baseline_embedders
cd baseline_embedders
git clone https://huggingface.co/FacebookAI/roberta-base
git clone https://huggingface.co/FacebookAI/roberta-large
cd ../

Then, the command to train different version of EasyRec is as follows:

• EasyRec-Small

  python create_roberta_small.py # create a small version of RoBERTa
  sh train_small.sh

• EasyRec-Base

  sh train_base.sh

• EasyRec-Large

  sh train_large.sh

During training, we utilize multiple GPUs to train EasyRec. You can adjust the number of GPUs in the shell file according to your server configuration. After training, the model parameters will be saved in the checkpoints folder.

📈 Evaluation

You can directly utilize the provided checkpoints on Hugging Face for evaluation or use the checkpoints obtained after training. Here, we provide example code for utilizing the checkpoints from Hugging Face.

Text-based Zero-shot Recommendation

To evaluate EasyRec for text-based zero-shot recommendation, we conduct experiments on the movies dataset. First, run the following commands to encode the text embeddings for user/item profiles under these three datasets (to utilize the checkpoints obtained from your own training, simply change the argument to --model ./checkpoints/easyrec-roberta-large):

python encode_easyrec.py --model hkuds/easyrec-roberta-base --cuda 0

To encode text embeddings for all models:

sh encode.sh

Then, conduct the evaluation (only the model name for the argument --model here):

python eval_text_emb.py --model easyrec-roberta-base --cuda 0

To evaluate all models:

sh evaluate.sh

Since there are 3 diversified profiles for both user and item in each dataset, we perform the evaluation 1+3 times with the corresponding text embeddings and calculate the mean value as the final result.

Text-enhanced Collaborative Filtering

We conduct evaluation on text-enhanced collaborative filtering performance on the steam dataset. To evaluate EasyRec, please ensure that you have already encoded text embeddings for the steam dataset with the command:

python encode_easyrec.py --model hkuds/easyrec-roberta-base --cuda 0

Then, navigate to the cf_rec folder by running cd cf_rec. After that, execute the following commands to evaluate:

• Base model

  python run.py --model {model_name} --dataset movies --cuda 0

  or

  sh cf_base.sh

• Text-enhanced model

  python run.py --model {model_name}_plus --semantic easyrec-roberta-base --dataset movies --cuda 0

  or

  sh cf_text_enhanced.sh

Supported Models: gccf and lightgcn.

🔮 Profile Generation and Diversification

Here we provide some examples with Amazon-Arts Data to conduct user/item profile generation and diversification.

Firstly, we need to complete the following three steps.

• Install the openai library pip install openai
• Prepare your OpenAI API Key
• Enter your key on Line 5 of these files: generation/generate_profile.py and generation/diverse_profile.py.

Then, here are the commands to generate the desired output with examples:

• Profile Generation:

  python generation/generate_profile.py

• Profile Diversification:

  python generation/diverse_profile.py

😀 The instructions we designed are saved in the generation/instruction folder. You can modify them according to your requirements and generate the desired output!

🌟 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!