doublejone831 / fire

This is the official implementation of FIRE

Hi there👋.

This repository is the official implementation of the paper FIRE: Fast Incremental Recommendation with Graph Signal Processing, which is accepted by The Web Conference 2022.

If you use the code in your work, please cite our paper.

How to run the code

Step 1: Check the compatibility of your python packages, we recommend you to use the following setting that has passed our test if you want to reproduce the results.

numpy == 1.19.4
pandas == 1.1.4
python == 3.6.11
scipy == 1.5.3
sklearn == 0.23.2
sparsesvd == 0.2.2

Step 2: prepare the dataset.

• If you use the default datasets (Movielens 1M or Douban Movie), please unzip the dataset under the directory dataset/ first;

• If you use your own dataset, you should make sure that the format of your dataset is compatible with the setting (i.e. the following variables) in dataloader.py

  • sep : the separation symbol between columns in dataset (default: '\t')
  • header_name: the header name of data frame generated from dataset (default: ['u', 'i', 'r', 't'])
  • pos_type: The type of positive interactions (default: [4.0, 5.0]).

  Note: You should disable the comment of Line 23-27 in dataloader.py to generate a new column named 'm' when loading data if you use your own dataset.

Step 3: Run the model.

• For Movielens 1M dataset:

  python main.py --dataset ml1m  --use_user_si --use_item_si

• For Douban Movie dataset:

  python main.py --dataset douban_movie --num_his_month 9 --num_cur_month 1 --decay_factor 1e-8 --pri_factor 128 --alphas '[0.2,1.0,0,0.2]' --use_item_si --user_threshold 0 --item_threshold 0.6