Equivariant Learning for Out-of-Distribution Cold-start Recommendation

This is the pytorch implementation of our paper

Equivariant Learning for Out-of-Distribution Cold-start Recommendation

Environment

• Anaconda 3
• python 3.7.11
• pytorch 1.10.0
• numpy 1.21.4

Usage

Data

The experimental data are in './data' folder.

Training

python main.py --model_name=$1 --data_path=$2 --batch_size=$3 --l_r=$4 --reg_weight=$5 --num_neg=$6 --lr_lambda=$7 --num_sample=$8 --temp_value=$9 --dim_E=$10 --alpha=$11 --pos_ratio=$12 --neg_ratio=$13 --align_all=$14 --mse_weight=$15 --log_name=$16 --gpu=$17

or use run.sh

sh run.sh CLCRec micro-video 256 0.001 0.001 512 0.1 0.7 1 128 0.9 0.1 0.1 0 0.01 log 0

• The log file will be in the './code/log/' folder.
• The explanation of hyper-parameters can be found in './code/main.py'.
• The default hyper-parameter settings are detailed in './code/hyper-parameters.txt'.

Inference

Get the results of EQUAL with Implicit Alignment Module (IAM) by running inference.py:

python inference.py --backmodel=$1 --drop_obj=$2 --dropout=$3 --topN=$4 --log_name=$5 --gpu=$6

or use inference.sh

sh inference.sh CLCRec model [0,0.05,0.1,0.15,0.2] 100 log 0

The explanation of hyper-parameters can be found in './code/inference.py'. The default hyper-parameter settings are detailed in './code/hyper-parameters.txt'.

Examples

1. Train EQUAL on micro-video:

cd ./code
sh run.sh CLCRec micro-video 256 0.001 0.001 512 0.1 0.7 1 128 0.9 0.1 0.1 0 0.01 log 0

2. Inference on Amazon:

cd ./code
python inference.py --backmodel CLCRec --drop_obj model --dropout [0,0.05,0.1,0.15,0.2] --topN 50 --log_name log --gpu 0