Introduction to gbiz_torch

A comprehensive toolkit package designed to help you accurately predict key metrics such as Click-Through Rates (CTR), Conversion Rates (CVR), uplift, and # strategies. Built with state-of-the-art algorithms and user-friendly interfaces, our package streamlines the process of forecasting and decision-making, allowing you to make data-driven choices with confidence. Whether you're looking to optimize your marketing campaigns, boost sales conversions, or fine-tune your # model, our package provides the insights you need to succeed in today's competitive market.

Tutorial

You can learn to use the package by referring to the examples in the directory ./example

More solution examples will be released soon~

Useful Eval Matrix

The following eval matrix has been implemented:

#	Eval Matrix	Explanation	Note
1	AUC	Area Under the ROC Curve	For Classification
2	Confusion_Matrix	Confusion Matrix is a performance measurement for classification	For Classification
3	ACC_F1_score	Accuracy, Macro-F1 and Weighted-F1	For Classification
4	Top_K_Acc	top_k_accuracy_score	For Classification
5	Multi_Class_RP	Multi Class precision, recall and F-beta	For Classification
6	r2_score	R2_score	For Classification
7	MAE	Mean Absolute Error	For Regression
8	MSE	Mean Square Error	For Regression
9	MAPE	Mean Absolute Percentage Error	For Regression
10	tsne	t-distributed stochastic neighbor embedding	For Manifold
11	sp_emb	spectral decomposition to the corresponding graph laplacian	For Manifold

Universal Layers in Commercial Algorithms

The models currently implemented in recommendation algorithms:

#	Model Name	model	Note
1	Wide and Deep	WndModel	Traditional recommendations
2	DNN	DNNModel	Traditional recommendations
3	DeepFM	DeepFMModel	Traditional recommendations
4	Deep and Cross	DCNModel	Traditional recommendations
5	NFM	NFMModel	Traditional recommendations
6	Tower	TowerModel	Traditional recommendations
7	FLEN	FLENModel	Traditional recommendations
8	Fibinet	FiBiNetModel	Traditional recommendations
9	InterHAt	InterHAtModel	Traditional recommendations
10	CAN	CANModel	Traditional recommendations
11	MaskNet	MaskNetModel	Traditional recommendations
12	ContextNet	ContextNetModel	Traditional recommendations
13	EDCN	EDCNModel	Traditional recommendations
14	BertSeq	Bert4RecModel	Sequence recommendation
15	GRU4Rec	GRU4RecModel	Sequence recommendation
16	DIN	DINModel	Sequence recommendation
17	DCAP	DCAPModel	Sequence recommendation
18	FBAS	FBASModel	Sequence recommendation
19	ESMM	ESMMModel	Multi objective recommendation
20	MMoE	GeneralMMoEModel	Multi objective recommendation
21	Hard Sharing	HardSharingModel	Multi objective recommendation
22	Cross Sharing	CrossSharingModel	Multi objective recommendation
23	Cross Stitch	CrossStitchModel	Multi objective recommendation
24	PLE	PLEModel	Multi objective recommendation

Universal Layers in Commercial Algorithms

In the consolidated algorithms, the following Layer networks have been implemented, which can be conveniently called by higher-level models, or users can directly call the Layer layers to assemble their own models.

#	Graph-based Layer	Note
1	HOMOGNNLayer	General GNN layers for Homogeneity Graph (GCNConv, GATConv, SAGEConv, TransformerConv, ARMAConv)
2	HETEGNNLayer	General GNN layers for heterogeneous Graph (HGTConv,HANConv)

#	Layer	Note
1	DNNLayer	DNN Net
2	FMLayer	FM Net in DeepFM, NFM
3	CrossLayer	Cross Net in Deep and Cross
4	CINLayer	CIN Net in XDeepFM
5	MultiHeadAttentionLayer	multi head attention in Bert
6	SelfAttentionLayer	scaled dot self attention in Bert
7	LayerNorm	Layer Normalization in Bert
8	PositionWiseFeedForwardLayer	Position wise feed forward in Bert
9	TransformerLayer	Transformer(including multi head attention and LayerNorm) in Bert
10	TransformerEncoder	Multi-Transformer in Bert
11	AutoIntLayer	Similar with TransformerLayer
12	FuseLayer	Local Activation Unit in DIN
13	SENETLayer	Squeeze and Excitation Layer
14	FieldWiseBiInteractionLayer	FM and MF layer in FLEN
15	CrossStitchLayer	Cross-stitch Networks for Multi-task Learning
16	GeneralMMoELayer	Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
17	Dice	Dice activation function
18	PositionEncodingLayer	Positional Encoding Layer in Transformer
19	CGCGatingNetworkLayer	task and expert Net in PLE
20	BiLinearInteractionLayer	Last feature net in Fibinet
21	CoActionLayer	co-action unit layer in CAN
22	MaskBlockLayer	MaskBlockLayer in MaskNet

Citation

If you find this code useful in your research, please cite it using the following BibTeX:

@software{
  Wang_gbiz_torch_A_comprehensive_2023,
  author = {Wang, Haowen},
  doi = {10.5281/zenodo.10222799},
  month = nov,
  title = {{gbiz_torch: A comprehensive toolkit for predicting key metrics in e-commercial fields}},
  url = {https://github.com/whw199833/gbiz_torch},
  version = {2.0.4},
  year = {2023}
}

or following APA:

Wang, H. (2023). gbiz_torch: A comprehensive toolkit for predicting key metrics in e-commercial fields (Version 2.0.4) [Computer software]. https://doi.org/10.5281/zenodo.10222799

Contact

If you have some questions or some advice, or want to contribute to this repo, do not hesitate to contact me:

mail: wanghw@zju.edu.cn

wechat: whw199833