/Understanding-the-Ranking-Loss

Primary LanguageJupyter Notebook

Understanding the Ranking Loss for Recommendation with Sparse User Feedback

This repository primarily focuses on disclosing experimental and analytical code pertaining to understanding ranking loss. The repository is currently under construction.

Main Idea

Recent advancements suggest combining BCE loss with ranking loss has shown substantial performance improvement in many industrial deployments. However, the efficacy of this combination loss is not fully understood. We identify a novel challenge in CTR prediction with BCE loss: gradient vanishing in negative samples. We then propose a novel perspective on the effectiveness of ranking loss in CTR prediction, that it leads to larger gradients on negative samples and hence mitigates their optimization issue, resulting in better classification ability.

Getting Start

Data Preparation

The Criteo dataset serves as a prevalent benchmark dataset in the field of click-through rate (CTR) prediction, encompassing approximately one week's worth of click-through data pertinent to display advertising. Please download the criteo_x1 data into /data/criteo folder. Kindly be informed that the usage of this dataset is restricted to academic research purposes exclusively and it must not be utilized for any commercial or illegal activities.

If you want to reproduce the negative sampling experiment, please use the /data/criteo/make_negative_sampling.ipynb to produce the negative sampled data. If you have no need, just skip it.

Configuration

We put our config files on config/criteo path. The basic settings of dataset and model are dataset_config.yaml and model_config.yaml, respectively.

Reproduce the results

We use the DCN v2 as backbone and adjust the sparsity of positive feedback for experiments.

  • BCE method (on sparse positive feedback scenario) The realization file of BCE method is named as DCNv2PosWeight.py, you can run the following command to rerun the experiment using gpu (index 0): python3 run_expid.py --expid DCNv2_criteo_x1_posw0.1 --config ./config/criteo_x1 --gpu 0
    • expid: the experiment id in model_config.yaml
    • config: the path containing model_config.yaml
    • --gpu: index of cuda

For producing rebust results, we run the each experiment 5 times and culculate the average results. Thus, you can run the following command to reproduce the results with 5 gpus: python3 run_param_tuner.py --config ./config/criteo_x1/DCNv2_criteo_x1_posw_tuner.yaml --gpu 0 1 2 3 4 The contents of this yaml entail an exploration across six distinct levels of sparsity, with each experiment being repeated five times.

  • Combined-Pair

python3 run_param_tuner.py --config ./config/criteo_x1/DCNv2_criteo_x1_poswall_sample_tuner.yaml --gpu 0 1 2 3 4

  • JRC

python3 run_param_tuner.py --config ./config/criteo_x1/DCNv2_criteo_x1_posw0.1_jrc_alpha0.4_rerun_tuner.yaml --gpu 0 1 2 3 4

  • RCR (ListCE)

python3 run_param_tuner.py --config ./config/criteo_x1/DCNv2_criteo_x1_posw0.1_listce_alpha0.9.yaml --gpu 0 1 2 3 4

  • Combined-List

python3 run_param_tuner.py --config ./config/criteo_x1/DCNv2_criteo_x1_posw0.1_combined_list_alpha0.9.yaml --gpu 0 1 2 3 4

  • Focal Loss

python3 run_param_tuner.py --config ./config/criteo_x1/DCNv2_criteo_x1_posw0.1_focal_gamma_tuner1.yaml --gpu 0 1 2 3 4

Analysing

The project conducted a comprehensive experimental analysis, with the corresponding analysis code housed within the analysis_pool.

Citation

If you find our code or propcessed data helpful in your research, please kindly cite the following papers.

@misc{lin2024understanding,
      title={Understanding the Ranking Loss for Recommendation with Sparse User Feedback}, 
      author={Zhutian Lin and Junwei Pan and Shangyu Zhang and Ximei Wang and Xi Xiao and Shudong Huang and Lei Xiao and Jie Jiang},
      year={2024},
      eprint={2403.14144},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}

Our code is based on the FuxiCTR and BARS.

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.

Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang. BARS: Towards Open Benchmarking for Recommender Systems. The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2022.