bottergpt / wsdm2022-xmrec-top1-solution

WSDM2022-XMRec Top1 Solution

The Cross-Market Recommendation task of WSDM CUP 2022 is about finding solutions to improve individual recommendation systems in resource-scarce target markets by leveraging data from similar high-resource source markets. Finally, our team OPDAI won the first place with NDCG@10 score of 0.6773 on the leaderboard. The training framework and pipeline are shown in the figure below. And our solution to this task will be detailed in the technical report.

Steps to Run

1. ENV Setup

First, build our docker image

bash control_docker.sh

Second, set the conda env for TF1.11 within the docker image

# set the tf1.11 env
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh && bash ~/miniconda.sh -b
# source ~/.bashrc
cd /root/miniconda3/bin
source activate
conda create -y -n TF111 python=3.6 tensorflow-gpu=1.11 keras=2.2.4
# or use absolute path for conda:
# ~/miniconda3/bin/conda create -y -n TF111 python=3.6 tensorflow-gpu=1.11 keras=2.2.4
# conda activate TF111 
source activate TF111 
cd /home/workspace/src && /root/miniconda3/envs/TF111/bin/pip install -r requirements_tf_4lgcn_v1.txt

2. Run the script in the docker

2.1 Train with the cached features

cd /home/workspace/
# train_main
nohup /usr/bin/python train_main.py > train.log 2>&1 &

After running the above codes, you can find the submission from the directory: /home/workspace/OUTPUT/SUB/FINAL

2.2 Train everything from scratch

If you want to train everything from scratch (It will be quite time-consuming), you could follow the cmds as follows,

cd /home/workspace/
nohup bash run_feas.sh > run_feas.log 2>&1 &  
nohup /usr/bin/python train_main.py --use_pretrain 0 --retrain_all_retrieval 1 > train.log 2>&1 &

3. For new test_run.tsv set inference

t1_test_run_path=SET_T1_NEW_TEST_RUN_PATH
t2_test_run_path=SET_T2_NEW_TEST_RUN_PATH
nohup /usr/bin/python train_main.py --use_pretrain 0 --retrain_all_retrieval 0 --t1_test_run_path t1_test_run_path --t2_test_run_path t2_test_run_path > train.log 2>&1 &

# nohup /usr/bin/python train_main.py --use_pretrain 0 --retrain_all_retrieval 0 --t1_test_run_path /home/workspace/DATA/t1/test_run.tsv --t2_test_run_path /home/workspace/DATA/t2/test_run.tsv > train.log 2>&1 &

If you retrain all retrieval models, you could set retrain_all_retrieval as 1 for loading all new trained features from /home/workplace/OUTPUT/00_NEW, or you could just leave it as 0 for loading from our pretrained features located at /home/workplace/OUTPUT/pretrain_features.

4. Pretrained materials

You can start from our pretrained models located at https://drive.google.com/drive/folders/16wBK4ydrt2Bk1qR_rFhZoGkOCy7ey6a4?usp=sharing.

Contact

If you have any questions, please contact us via [e-mail].

Reference

• WSDM 2022 CUP - Cross-Market Recommendation - Starter Kit
• wsdm-itemcf-baseline
• lightgcn-tf
• lightgcn
• Solution to the Debiasing Track of KDD CUP 2020 (Team Rush)
• KDDCUP_2020_Debiasing_1st_Place
• GraphEmbedding