/FiBiNet

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FiBiNet++:Improving FiBiNet by Effectively Reducing Model Parameters for CTR Prediction

Code of FiBiNet++:Improving FiBiNet by Effectively Reducing Model Parameters for CTR Prediction.

Also support FiBiNet of paper FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction .

Prerequisites

  • Python == 3.6
  • TensorFlow-GPU == 1.14

Getting Started

Installation

  • Install TensorFlow-GPU 1.14

  • Clone this repo

Dataset

  • Links of datasets are:

  • You can download the original datasets and preprocess them by yourself. Run python -u -m fibinet.preprocessing.{dataset_name}.{dataset_name}_process to preprocess the datasets. dataset_name can be criteo or avazu.

  • This repo also contains a demo dataset of criteo, which contains 100,000 samples and has been preprocessed. It is used to help demonstrate FiBiNet++.

Training

You can use python -u -m fibinet.run --version {version} --config {config_path} to train a specific model on a dataset.

Some important parameters are list below, and other hyper-parameters can be found in the code.

  • version: model version, supports v1, ++, and custom, default to ++. For custom, you can adjust all parameter values flexibly.
  • config_path: specifies the paths of the input/output files and the fields of the dataset. It is generated during dataset preprocessing. Support values: ./config/criteo/config_dense.json, ./config/avazu/config_sparse.json.
  • mode: running mode, supports train, retrain, test.

Acknowledgement

Part of the code comes from DeepCTR.