User-controllable Recommendation Against Filter Bubbles

This is the pytorch implementation of our paper at SIGIR 2022:

User-controllable Recommendation Against Filter Bubbles

Wenjie Wang, Fuli Feng, Liqiang Nie, Tat-Seng Chua.

Environment

• Anaconda 3
• python 3.7.3
• pytorch 1.4.0
• numpy 1.16.4

Usage

Data

• The three datasets are released in the './data' folder.

Code

• The code for training and inference is in the './code' folder.
• FM and NFM are first well trained, and then UCI is used for inference.
• We have user-feature controls (i.e., C-UCI and F-UCI), and item-feature controls (i.e., Reranking, C-UCI, and F-UCI).

FM and NFM Training

python main.py --model=$1 --dataset=$2 --hidden=$3 --layers=$4 --lr=$5 --batch_size=$6 --dropout=$7 --lamda=$8 --batch_norm=$9 --epochs=$10 --log_name=$11 --gpu=$12

• The explanation of hyper-parameters can be found in './code/FM_NFM/main.py'.
• The well trained models are provided in './code/FM_NFM/best_models'. We have tuned the hyper-parameters and chosen the best ones.

UCI Inference

item-feature controls

• Reranking

cd item_controls/Reranking
python UCI_reranking.py --model=FM --dataset=ml_1m

• Fine-grained and coarse-grained item-feature controls

cd item_controls/fine_coarse_UCI
python C_UCI_inference.py --model=FM --dataset=ml_1m
python F_UCI_inference.py --model=NFM --dataset=amazon_book

user-feature coarse-grained controls

• UCI and maskUF for FM and NFM

cd user_coarse_controls
python UCI_coarse_user_control.py --model=FM

• Inference for vanilla FM and NFM

python FM_NFM_inference.py --model=FM

Note that we only use DIGIX for the experiments of user-feature controls.

user-feature fine-grained controls

• UCI and changeUF for FM and NFM

cd user_fine_controls
python UCI_fine_user_control.py --model=FM

• Inference for vanilla FM and NFM

python FM_NFM_inference.py --model=FM

Acknowledgment

Thanks to the FM/NFM implementation:

• NFM-torch from Yangyang Guo.
• NFM-tensorflow from Xiangnan He.

License

NUS © NExT++