Implementation of SAQRec

This is the official implementation of the paper "SAQRec: Aligning Recommender Systems to User Satisfaction via Questionnaire Feedback" based on PyTorch.

Overview

The main implementation of SAQRec can be found in the file models/SAQRec.py.

Reproduction

Check the following instructions for reproducing experiments.

Experimental Setting

All the hyper-parameter settings of SAQRec on both datasets can be found in files config/SAQRec_commercial.yaml and config/SAQRec_kuairand.yaml. The settings of two datasets can be found in file config/const.py.

Dataset

Check folder data for details.

Quick Start

1. Download and process data

Place data files in the folder data.

2. Satisfy the requirements

Our experiments were done with the following python packages:

python==3.8.18
torch==1.10.1
numpy==1.24.1
scikit-learn==1.3.1
tqdm==4.66.1
PyYAML==6.0.1

3. Train and evaluate our model:

Run codes in command line:

# Base model
python main.py --name base --workspace ./workspace/base --gpu_id 0  --epochs 100 --model Base  --batch_size 512 --dataset_name kuairand --num_negs 2

# Propensity model 
python main.py --name pro --workspace ./workspace/pro --gpu_id 0  --epochs 100 --model Pro_Model  --batch_size 512 --dataset_name kuairand --num_negs 0

# Satisfaction model 
python main.py --name satis --workspace ./workspace/satis --gpu_id 0  --epochs 100 --model Satis_Model  --batch_size 512 --dataset_name kuairand --num_negs 0

# SAQRec 
python main.py --name SAQRec --workspace ./workspace/SAQRec --gpu_id 0  --epochs 100 --model SAQRec  --batch_size 512 --dataset_name kuairand --num_negs 2

Note: When training propensity and satisfaction models, comment out the test section of the main.py

4. Check training and evaluation process:

After training, check log files, for example, workspace/SAQRec/log/default.log.

Environments

We conducted the experiments based on the following environments:

• CUDA Version: 11.1
• OS: CentOS Linux release 7.4.1708 (Core)
• GPU: The NVIDIA® T4 GPU
• CPU: Intel(R) Xeon(R) Gold 6230R CPU @ 2.10GHz