Legommenders v2

A Modular Framework for Recommender Systems in the Era of LLMs

Installation

gh repo clone Jyonn/Legommenders
cd Legommenders
pip install -r requirements.txt

Quick Start with Three Steps

Data Preprocessing

python process.py --data mind

Token Embedding Extraction

python embed.py --model bertbase

Training a Recommender

NAML

python trainer.py --data config/data/mind.yaml --model config/model/naml.yaml --hidden_size 256 --lr 0.001 --batch_size 64 --item_page_size 0 --embed config/embed/glove.yaml

NRMS

python trainer.py --data config/data/mind.yaml --model config/model/nrms.yaml --hidden_size 256 --lr 0.001 --batch_size 64 --item_page_size 0 --embed config/embed/glove.yaml

PLMNR-NAML

python trainer.py 
  --data config/data/mind-lm.yaml 
  --model config/model/bert-naml.yaml 
  --hidden_size 256 
  --lr 0.0001 
  --batch_size 64 
  --item_page_size 128  # set it as large as possible based on your GPU memory  
  --embed config/embed/bert.yaml  # generate the yaml first, by running python embed.py --model bertbase
  --use_lora 1 
  --lora_r 8 
  --lora_alpha 128 
  --lm bert  # indicate the language model
  --tune_from -2  # freeze the first N-1 layers, and tune the last layer, it is the same as --tune_from 10

ONCE-DIRE-LLAMA1-NAML

python trainer.py 
  --data config/data/mind-lm-prompt.yaml  # for more powerful language models, we suggest to use the data concatenated with natural prompts
  --model config/model/llama-naml.yaml 
  --hidden_size 256 
  --lr 0.0001 
  --batch_size 64 
  --item_page_size 64 
  --embed config/embed/llama.yaml  # generate the yaml first, by running python embed.py --model llama1
  --use_lora 1 
  --lora_r 32 
  --lora_alpha 128 
  --lm llama1 
  --tune_from -2  # freeze the first N-1 layers, and tune the last layer, it is the same as --tune_from 30

More documentations will be available soon.

Updates

Jan. 6, 2025

• Legommenders v2.0 beta is released!

Dec. 5, 2024

• LSTUR model is now re-added to the Legommenders package, which was not compatible from Jan. 2024.
• LLMs can be used for item encoder.

Jan. 23, 2024

• Legommenders partially supports the flatten sequential recommendation model.
• New models are added, including: MaskNet, GDCN, etc.

Oct. 16, 2023

• We clean the code and convert names of the item-side parameters.

Oct. 5, 2023

• The first recommender system package, Legommenders, with a modular-design is released!
• Legommenders involves a set of recommender system algorithms, including:
  • Matching based methods: NAML, NRMS, LSTUR, etc.
  • Ranking based methods: DCN, DeepFM, PNN, etc.

Citations

Legommenders have served as a fundamental framework for several research projects, including ONCE, SPAR,GreenRec, and UIST. If you find Legommenders useful in your research, please consider citing our project:

@article{legommenders,
  title={Legommenders: A Comprehensive Content-Based Recommendation Library with LLM Support},
  author={Liu, Qijiong and Fan, Lu and Wu, Xiao-Ming},
  journal={arXiv preprint arXiv:2412.15973},
  year={2024}
}