🐧 Implementation of GFN4Retention on KuaiSim for Kuairand-Pure Dataset

conda create -n KRL python=3.8
conda activate KRL
conda install pytorch torchvision -c pytorch
conda install pandas matplotlib scikit-learn tqdm ipykernel
python -m ipykernel install --user --name KRL --display-name "KRL"

See preprocess/KuaiRandDataset.ipynb for details

1.1 Immediate User Response Model

Example raw data format in preprocessed KuaiRand:

(session_id, request_id, user_id, video_id, date, time, is_click, is_like, is_comment, is_forward, is_follow, is_hate, long_view)

Example item meta data format in preprocessed KuaiRand:

(video_id, video_type, upload_type, music_type, log_duration, tag)

Example user meta data format in preprocessed KuaiRand:

(user_active_degree, is_live_streamer, is_video_author, follow_user_num_range, fans_user_num_range, friend_user_num_range, register_days_range, onehot_feat{0,1,6,9,10,11,12,13,14,15,16,17})

bash train_multi_behavior_user_response.sh

Note: multi-behavior user response models consists the state_encoder that is assumed to be the ground truth user state transition model.

1.2 User Retention Model

Pick a multi-behavior user response model for cross-session generation and retention model training, change the shell script accordingly (by setting the keyword 'KRMB_MODEL_KEY').

Generate user retention data in format:

(session_id, user_id, session_enc, return_day)

bash generate_session_data.sh

User retention happens after leaving the previous session and identifies the beginning of the next session.

Evaluation metrics and protocol

Return time is the average time gap between the last request of the session and the first request of the session.

User retention is the average ratio of visiting the system again.

Full model:

bash train_gfn_wif_kpure_cuminf_PAUR_crosssession.sh

Ablation without immediate feedback:

bash train_gfn_kpure_cuminf_PAUR_crosssession.sh

Training curves check:

TrainingObservation.ipynb