RLMRec: Representation Learning with Large Language Models for Recommendation

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

Representation Learning with Large Language Models for Recommendation
Xubin Ren, Wei Wei, Lianghao Xia, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, Chao Huang*

* denotes corresponding author

In this paper, we propose a model-agnostic framework RLMRec that aims to enhance existing recommenders with LLM-empowered representation learning. It proposes a recommendation paradigm that integrates representation learning with LLMs to capture intricate semantic aspects of user behaviors and preferences. RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals through a cross-view alignment framework.

Environment

The codes are written in Python 3.9.16 with the following dependencies.

• numpy == 1.24.3
• pytorch == 1.13.1 (GPU version)
• torch-scatter == 2.1.1
• torch-sparse == 0.6.17
• scipy == 1.10.1

😉 The codes are developed based on the SSLRec framework.

Dataset

We utilized three public datasets to evaluate RLMRec: Amazon-book, Yelp, and Steam.

First of all, please unzip the data by running following commands.

cd data/
cat data.tar.gz0* > data.tar.gz
tar zxvf data.tar.gz

Each dataset consists of a training set, a validation set, and a test set. During the training process, we utilize the validation set to determine when to stop the training in order to prevent overfitting.

- amazon(yelp/steam)
|--- trn_mat.pkl
|--- val_mat.pkl
|--- tst_mat.pkl
|--- usr_prf.pkl
|--- itm_prf.pkl
|--- usr_emb_np.pkl
|--- itm_emb_np.pkl

User/Item Profile

The usr_prf.pkl and itm_prf.pkl files store the generated profiles of users and items from ChatGPT. You can run the code python data/read_profile.py as an example to read the profiles.

Semantic Embedding

The encoded semantic embeddings from the user/item profiles are stored in usr_emb_np.pkl and itm_emb_np.pkl.

🤗 Welcome to use our processed data to improve your research!

Examples to run the codes

The command to evaluate the backebone models and RLMRec is as follows.

• Backbone

  python encoder/train_encoder.py --model {model_name} --dataset {dataset} --cuda 0

• RLMRec-Con (Constrastive Alignment):

  python encoder/train_encoder.py --model {model_name}_plus --dataset {dataset} --cuda 0

• RLMRec-Gen (Generative Alignment):

  python encoder/train_encoder.py --model {model_name}_gene --dataset {dataset} --cuda 0

Supported models/datasets:

• model_name: gccf, lightgcn, sgl, simgcl, dccf, autocf
• dataset: amazon, yelp, steam

Hypeparameters:

• The hyperparameters of each model are stored in encoder/config/modelconf (obtained by grid-search).

For advanced usage of arguments, run the code with --help argument.

Thanks for your interest in our work.