RecSysChallenge2024_DIN

This repository is built on top of FuxiCTR, a configurable, tunable, and reproducible library for CTR prediction.

Paper reference FuxiCTR:

• Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, Xiuqiang He. Open Benchmarking for Click-Through Rate Prediction. The 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021.

Setup virtual environment

If you want to use venv

1. Please set up the environment as follows (we used python 3.9 and python 3.10).

   python3 -m venv recsys_din
   source recsys_din/bin/activate
   python -m pip install --upgrade pip
   pip install --no-cache-dir -r requirements.txt
   

If you want to use Docker

1. make sure you have started the docker engine
2. Build the container:

      docker build -t recsyschallenge2024_din .

3. Run the container

      docker run -d -it --name recsyschallenge_container  recsyschallenge2024_din /bin/bash

4. Access the terminal of the container

   docker exec -it recsyschallenge_container /bin/sh

Data Preparation and Model Training (DIVAN and VDIN)

1. Download and preprare data

   python prepare_data_v1.py --size large --test --embedding_size 64 --neg_sampling

2. Train the model on train and validation sets:

   • Train VDIN

     python run_param_tuner.py --config config/VDIN_ebnerd_large_x1_tuner_config_01.yaml --gpu 0

   • Train DIVAN

     python run_param_tuner.py --config config/DIVAN_ebnerd_large_x1_tuner_config_01.yaml --gpu 0

3. Make predictions on the test set:

   Get the experiment_id from running logs or the result csv file [VDIN|DIVAN]_ebnerd_large_x1_tuner_config_01.csv, and then you can run prediction on the test.

   python submit.py --config config/[VDIN|DIVAN]_ebnerd_large_x1_tuner_config_01 --expid [VDIN|DIVAN]_ebnerd_large_x1_001_1860e41e --gpu 0

Data preparation and prediction with PopularRanker and ViralRanker

1. Download and preprare data

   python prepare_data_pop_and_vir_scores.py --size large --test

2. Test the model on the validation set:

   python run_[popular|virality]_expid.py

3. Make predictions on the test set:

    python submit_[popular|viral].py