LDA-MRS

This repository contains our implementations for "Lightweight Denoising and Aligning Multi-modal Recommender System".

Folder structure

• data: The public links to download the datasets and dataset preprocessing methods.
• log_files: The log files to quickly reproduce the reported results.
• src_LDAMRS: The implementation of LDA-MRS on Amazon Baby, Sports and Clothing.

Dependencies

• OS: Ubuntu 20.04.6 LTS
• numpy==1.23.5
• pandas==1.5.3
• python==3.10.9
• scipy==1.10.0
• torch==2.0.0
• pyyaml==6.0
• pyg==2.3.0
• networkx==2.8.4
• tqdm==4.65.0
• lmdb==1.4.0

Main Experiment

1. Step 1: Download and pre-process the datasets to folder './data' following the instruction in './data/Readme.md'.

2. Step 2: Run the following command to construct item-item behavior graph.

   # construct item-item behavior graph. 
   
   python -u build_iib_graph.py --dataset=baby --topk=2

   There are two parameters:

   1. dataset: str type, allowed values are baby, sports, clothing, and tiktok.
   2. topk: int type, parameter for pruning the Item-item behavior graph.

3. Step 3: Specify configurations

   1. Enter to the './src_LDAMRS' folder

      # enter to the src folder
      
      cd src_LDAMRS

   2. Specify dataset-specific configurations

      vim configs/dataset/xx.yaml

   3. Specify model-specific configurations

      vim configs/model/xx.yaml

   4. Specify the overall configurations

      vim configs/overall.yaml

   5. Rename the model filename

      # when run the lightgcn-ldarms-s
      cp models/lightgcn-ldarms-s.py models/lightgcn.py
      
      # when run the lightgcn-ldarms-d
      cp models/lightgcn-ldarms-d.py models/lightgcn.py

4. Step 4: Run the following command to train and evaluate the model.

   # run the code 
   
   python -u main.py --model=LightGCN --dataset=baby --gpu_id=0

   There are three parameters:

   1. model: str type, the name of the backbone model, such as LightGCN.

   2. dataset: str type, the name of the dataset, such as baby, sports, clothing.

   3. gpu_id: str type, the specified GPU.

Acknowledgement

The structure of this code is based on MMRec. Thanks for their work.